Overview

Dataset statistics

Number of variables9
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory74.1 B

Variable types

Numeric1
Categorical4
Text4

Dataset

Description중소기업제품서비스센터현황은 서비스센터, 관할지역, 처리유형, 센터명, 센터주소, 연락처 데이터를 제공합니다.(일부 연락처는 비식별화 처리)
Author중소벤처기업부
URLhttps://www.data.go.kr/data/3045171/fileData.do

Alerts

순번 is highly overall correlated with 서비스센터High correlation
서비스센터 is highly overall correlated with 순번High correlation
관할지역 is highly overall correlated with 지역1High correlation
지역1 is highly overall correlated with 관할지역High correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:34:19.098835
Analysis finished2023-12-12 17:34:19.871240
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.5
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T02:34:19.938918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q130.75
median60.5
Q390.25
95-th percentile114.05
Maximum120
Range119
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation34.785054
Coefficient of variation (CV)0.57495957
Kurtosis-1.2
Mean60.5
Median Absolute Deviation (MAD)30
Skewness0
Sum7260
Variance1210
MonotonicityStrictly increasing
2023-12-13T02:34:20.320857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
62 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%

서비스센터
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
위니아에이드
54 
에스케이네트웍스서비스
35 
SG전자통신
31 

Length

Max length11
Median length6
Mean length7.4583333
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위니아에이드 54
45.0%
에스케이네트웍스서비스 35
29.2%
SG전자통신 31
25.8%

Length

2023-12-13T02:34:20.470522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:34:20.586559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위니아에이드 54
45.0%
에스케이네트웍스서비스 35
29.2%
sg전자통신 31
25.8%

관할지역
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
경기
25 
서울
18 
강원
10 
경남
10 
경북
9 
Other values (14)
48 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
경기 25
20.8%
서울 18
15.0%
강원 10
 
8.3%
경남 10
 
8.3%
경북 9
 
7.5%
대구 6
 
5.0%
인천 5
 
4.2%
전북 5
 
4.2%
전남 4
 
3.3%
광주 4
 
3.3%
Other values (9) 24
20.0%

Length

2023-12-13T02:34:20.695013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 25
20.8%
서울 18
15.0%
강원 10
 
8.3%
경남 10
 
8.3%
경북 9
 
7.5%
대구 6
 
5.0%
인천 5
 
4.2%
전북 5
 
4.2%
부산 4
 
3.3%
광주 4
 
3.3%
Other values (9) 24
20.0%

지역1
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
경기
18 
서울
16 
강원
 
6
경북
 
6
경남
 
5
Other values (44)
69 

Length

Max length5
Median length2
Mean length2.1083333
Min length2

Unique

Unique30 ?
Unique (%)25.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 18
 
15.0%
서울 16
 
13.3%
강원 6
 
5.0%
경북 6
 
5.0%
경남 5
 
4.2%
부산 4
 
3.3%
대구 4
 
3.3%
대전 3
 
2.5%
광주 3
 
2.5%
전북 3
 
2.5%
Other values (39) 52
43.3%

Length

2023-12-13T02:34:20.839858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 18
 
15.0%
서울 16
 
13.3%
강원 6
 
5.0%
경북 6
 
5.0%
경남 5
 
4.2%
부산 4
 
3.3%
대구 4
 
3.3%
대전 3
 
2.5%
광주 3
 
2.5%
전북 3
 
2.5%
Other values (39) 52
43.3%

지역2
Text

Distinct61
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T02:34:21.092821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.1583333
Min length2

Characters and Unicode

Total characters259
Distinct characters64
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

Unique34 ?
Unique (%)28.3%

Sample

1st row금천
2nd row금천
3rd row서울/경기
4th row충청
5th row강북
ValueCountFrequency (%)
수도권 13
 
10.8%
경상 10
 
8.3%
전라 8
 
6.7%
북구 4
 
3.3%
충청 3
 
2.5%
강릉 3
 
2.5%
고양 3
 
2.5%
수원 3
 
2.5%
금천 3
 
2.5%
강원 2
 
1.7%
Other values (51) 68
56.7%
2023-12-13T02:34:21.470883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 17
 
6.6%
구 15
 
5.8%
도 13
 
5.0%
권 13
 
5.0%
경 11
 
4.2%
주 11
 
4.2%
전 10
 
3.9%
원 10
 
3.9%
상 10
 
3.9%
천 9
 
3.5%
Other values (54) 140
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
99.6%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 17
 
6.6%
구 15
 
5.8%
도 13
 
5.0%
권 13
 
5.0%
경 11
 
4.3%
주 11
 
4.3%
전 10
 
3.9%
원 10
 
3.9%
상 10
 
3.9%
천 9
 
3.5%
Other values (53) 139
53.9%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 258
99.6%
Common 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 17
 
6.6%
구 15
 
5.8%
도 13
 
5.0%
권 13
 
5.0%
경 11
 
4.3%
주 11
 
4.3%
전 10
 
3.9%
원 10
 
3.9%
상 10
 
3.9%
천 9
 
3.5%
Other values (53) 139
53.9%
Common
ValueCountFrequency (%)
/ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 258
99.6%
ASCII 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 17
 
6.6%
구 15
 
5.8%
도 13
 
5.0%
권 13
 
5.0%
경 11
 
4.3%
주 11
 
4.3%
전 10
 
3.9%
원 10
 
3.9%
상 10
 
3.9%
천 9
 
3.5%
Other values (53) 139
53.9%
ASCII
ValueCountFrequency (%)
/ 1
100.0%

처리유형
Categorical

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
출장/내방
80 
출장
30 
내방
9 
택배/수리센터
 
1

Length

Max length7
Median length5
Mean length4.0416667
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row택배/수리센터
2nd row내방
3rd row출장
4th row출장
5th row출장/내방

Common Values

ValueCountFrequency (%)
출장/내방 80
66.7%
출장 30
 
25.0%
내방 9
 
7.5%
택배/수리센터 1
 
0.8%

Length

2023-12-13T02:34:21.619159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:34:21.724567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
출장/내방 80
66.7%
출장 30
 
25.0%
내방 9
 
7.5%
택배/수리센터 1
 
0.8%

센터명
Text

Distinct118
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T02:34:22.025151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.4666667
Min length3

Characters and Unicode

Total characters536
Distinct characters84
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

Unique117 ?
Unique (%)97.5%

Sample

1st row택배센터
2nd row본사센터
3rd row본사센터
4th row본사센터
5th row노원센터
ValueCountFrequency (%)
지정점 35
 
22.6%
본사센터 3
 
1.9%
택배센터 1
 
0.6%
충주점 1
 
0.6%
구리 1
 
0.6%
신부평 1
 
0.6%
인천남동 1
 
0.6%
강남 1
 
0.6%
구로 1
 
0.6%
제주점 1
 
0.6%
Other values (109) 109
70.3%
2023-12-13T02:34:22.517712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 89
16.6%
정 37
 
6.9%
35
 
6.5%
지 35
 
6.5%
센 30
 
5.6%
터 30
 
5.6%
주 21
 
3.9%
구 16
 
3.0%
원 13
 
2.4%
산 13
 
2.4%
Other values (74) 217
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 495
92.4%
Space Separator 35
 
6.5%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 89
18.0%
정 37
 
7.5%
지 35
 
7.1%
센 30
 
6.1%
터 30
 
6.1%
주 21
 
4.2%
구 16
 
3.2%
원 13
 
2.6%
산 13
 
2.6%
천 12
 
2.4%
Other values (69) 199
40.2%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 495
92.4%
Common 39
 
7.3%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 89
18.0%
정 37
 
7.5%
지 35
 
7.1%
센 30
 
6.1%
터 30
 
6.1%
주 21
 
4.2%
구 16
 
3.2%
원 13
 
2.6%
산 13
 
2.6%
천 12
 
2.4%
Other values (69) 199
40.2%
Common
ValueCountFrequency (%)
35
89.7%
) 2
 
5.1%
( 2
 
5.1%
Latin
ValueCountFrequency (%)
G 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 495
92.4%
ASCII 41
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 89
18.0%
정 37
 
7.5%
지 35
 
7.1%
센 30
 
6.1%
터 30
 
6.1%
주 21
 
4.2%
구 16
 
3.2%
원 13
 
2.6%
산 13
 
2.6%
천 12
 
2.4%
Other values (69) 199
40.2%
ASCII
ValueCountFrequency (%)
35
85.4%
) 2
 
4.9%
( 2
 
4.9%
G 1
 
2.4%
S 1
 
2.4%

주소
Text

Distinct117
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T02:34:22.897354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length35.5
Mean length27.925
Min length14

Characters and Unicode

Total characters3351
Distinct characters270
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)96.7%

Sample

1st row서울특별시 금천구 가산디지털2로 187
2nd row서울특별시 금천구 가산디지털2로 187
3rd row서울특별시 금천구 가산디지털2로 187
4th row서울특별시 금천구 가산디지털2로 187
5th row서울 노원구 상계1동 1019-57(연호빌딩 2층)
ValueCountFrequency (%)
경기도 21
 
3.0%
2층 21
 
3.0%
1층 17
 
2.4%
3층 15
 
2.1%
서울특별시 13
 
1.8%
북구 8
 
1.1%
강원도 7
 
1.0%
남구 5
 
0.7%
전라북도 5
 
0.7%
경기 5
 
0.7%
Other values (476) 590
83.5%
2023-12-13T02:34:23.422302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
600
 
17.9%
1 126
 
3.8%
시 120
 
3.6%
2 112
 
3.3%
동 104
 
3.1%
구 97
 
2.9%
로 97
 
2.9%
) 75
 
2.2%
( 75
 
2.2%
층 69
 
2.1%
Other values (260) 1876
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1942
58.0%
Space Separator 600
 
17.9%
Decimal Number 579
 
17.3%
Close Punctuation 75
 
2.2%
Open Punctuation 75
 
2.2%
Dash Punctuation 33
 
1.0%
Other Punctuation 22
 
0.7%
Uppercase Letter 19
 
0.6%
Lowercase Letter 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 120
 
6.2%
동 104
 
5.4%
구 97
 
5.0%
로 97
 
5.0%
층 69
 
3.6%
도 58
 
3.0%
대 47
 
2.4%
경 46
 
2.4%
남 41
 
2.1%
광 36
 
1.9%
Other values (228) 1227
63.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
21.1%
S 3
15.8%
B 3
15.8%
N 2
10.5%
F 1
 
5.3%
D 1
 
5.3%
V 1
 
5.3%
K 1
 
5.3%
M 1
 
5.3%
G 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 126
21.8%
2 112
19.3%
3 68
11.7%
7 49
 
8.5%
0 48
 
8.3%
4 42
 
7.3%
6 38
 
6.6%
9 32
 
5.5%
8 32
 
5.5%
5 32
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
r 1
16.7%
v 1
16.7%
i 1
16.7%
c 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 21
95.5%
. 1
 
4.5%
Space Separator
ValueCountFrequency (%)
600
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1942
58.0%
Common 1384
41.3%
Latin 25
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 120
 
6.2%
동 104
 
5.4%
구 97
 
5.0%
로 97
 
5.0%
층 69
 
3.6%
도 58
 
3.0%
대 47
 
2.4%
경 46
 
2.4%
남 41
 
2.1%
광 36
 
1.9%
Other values (228) 1227
63.2%
Common
ValueCountFrequency (%)
600
43.4%
1 126
 
9.1%
2 112
 
8.1%
) 75
 
5.4%
( 75
 
5.4%
3 68
 
4.9%
7 49
 
3.5%
0 48
 
3.5%
4 42
 
3.0%
6 38
 
2.7%
Other values (6) 151
 
10.9%
Latin
ValueCountFrequency (%)
A 4
16.0%
S 3
12.0%
B 3
12.0%
N 2
 
8.0%
e 2
 
8.0%
r 1
 
4.0%
v 1
 
4.0%
F 1
 
4.0%
i 1
 
4.0%
D 1
 
4.0%
Other values (6) 6
24.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1942
58.0%
ASCII 1409
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600
42.6%
1 126
 
8.9%
2 112
 
7.9%
) 75
 
5.3%
( 75
 
5.3%
3 68
 
4.8%
7 49
 
3.5%
0 48
 
3.4%
4 42
 
3.0%
6 38
 
2.7%
Other values (22) 176
 
12.5%
Hangul
ValueCountFrequency (%)
시 120
 
6.2%
동 104
 
5.4%
구 97
 
5.0%
로 97
 
5.0%
층 69
 
3.6%
도 58
 
3.0%
대 47
 
2.4%
경 46
 
2.4%
남 41
 
2.1%
광 36
 
1.9%
Other values (228) 1227
63.2%

전화번호
Text

Distinct112
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T02:34:23.724627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.025
Min length9

Characters and Unicode

Total characters1443
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)90.8%

Sample

1st row1577-7818
2nd row1577-7818
3rd row1577-7818
4th row1577-7818
5th row02-933-8333
ValueCountFrequency (%)
010 5
 
4.2%
1577-7818 4
 
3.3%
062-527-6797 2
 
1.7%
053-242-7000 1
 
0.8%
063-286-9588 1
 
0.8%
070-4234-2770 1
 
0.8%
061-745-9588 1
 
0.8%
062-956-9589 1
 
0.8%
061-281-9588 1
 
0.8%
062-513-3069 1
 
0.8%
Other values (102) 102
85.0%
2023-12-13T02:34:24.218674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 237
16.4%
0 190
13.2%
5 130
9.0%
2 130
9.0%
3 128
8.9%
7 115
8.0%
6 106
7.3%
4 106
7.3%
1 98
6.8%
8 97
6.7%
Other values (3) 106
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1164
80.7%
Dash Punctuation 237
 
16.4%
Other Punctuation 40
 
2.8%
Math Symbol 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 190
16.3%
5 130
11.2%
2 130
11.2%
3 128
11.0%
7 115
9.9%
6 106
9.1%
4 106
9.1%
1 98
8.4%
8 97
8.3%
9 64
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 237
100.0%
Other Punctuation
ValueCountFrequency (%)
* 40
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1443
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 237
16.4%
0 190
13.2%
5 130
9.0%
2 130
9.0%
3 128
8.9%
7 115
8.0%
6 106
7.3%
4 106
7.3%
1 98
6.8%
8 97
6.7%
Other values (3) 106
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 237
16.4%
0 190
13.2%
5 130
9.0%
2 130
9.0%
3 128
8.9%
7 115
8.0%
6 106
7.3%
4 106
7.3%
1 98
6.8%
8 97
6.7%
Other values (3) 106
7.3%

Interactions

2023-12-13T02:34:19.613304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:34:24.359232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번서비스센터관할지역지역1지역2처리유형
순번1.0000.9520.8090.8640.8960.483
서비스센터0.9521.0000.3860.7080.8390.444
관할지역0.8090.3861.0000.9860.9120.000
지역10.8640.7080.9861.0000.0000.515
지역20.8960.8390.9120.0001.0000.000
처리유형0.4830.4440.0000.5150.0001.000
2023-12-13T02:34:24.479619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서비스센터관할지역처리유형지역1
서비스센터1.0000.2040.4360.362
관할지역0.2041.0000.0000.678
처리유형0.4360.0001.0000.214
지역10.3620.6780.2141.000
2023-12-13T02:34:24.626100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번서비스센터관할지역지역1처리유형
순번1.0000.9180.4460.4080.300
서비스센터0.9181.0000.2040.3620.436
관할지역0.4460.2041.0000.6780.000
지역10.4080.3620.6781.0000.214
처리유형0.3000.4360.0000.2141.000

Missing values

2023-12-13T02:34:19.711937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:34:19.825829image/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.

Sample

순번서비스센터관할지역지역1지역2처리유형센터명주소전화번호
01SG전자통신서울서울금천택배/수리센터택배센터서울특별시 금천구 가산디지털2로 1871577-7818
12SG전자통신서울서울금천내방본사센터서울특별시 금천구 가산디지털2로 1871577-7818
23SG전자통신서울서울서울/경기출장본사센터서울특별시 금천구 가산디지털2로 1871577-7818
34SG전자통신서울서울충청출장본사센터서울특별시 금천구 가산디지털2로 1871577-7818
45SG전자통신서울서울강북출장/내방노원센터서울 노원구 상계1동 1019-57(연호빌딩 2층)02-933-8333
56SG전자통신서울서울서초출장/내방서초센터서울 서초구 방배동 1007-2호 새빛빌딩 303호02-588-8101
67SG전자통신서울서울금천출장/내방서서울센터서울 금천구 시흥대로 97 B지원 BB36-1(시흥동,시흥유통상가)02-6369-5596
78SG전자통신인천인천부평출장/내방인천센터인천 부평구 열우물로 109 1층032-866-9922
89SG전자통신경기경기수원출장/내방수원센터경기 수원시 권선구 세류2동 537-19 설아빌딩 4층031-235-4566
910SG전자통신경기경기고양출장/내방일산센터경기 고양시 덕양구 토당동 870-5번지 대성빌딩 502호031-913-7333
순번서비스센터관할지역지역1지역2처리유형센터명주소전화번호
110111에스케이네트웍스서비스대전대덕구충청출장대전 지정점대전시 대덕구 한밭대로 1201 대성빌딩 302호042-636-2927
111112에스케이네트웍스서비스부산동래구경상출장/내방부산동래구 지정점부산 진구 가야대로666 2층051-512-2727
112113에스케이네트웍스서비스울산남구경상출장남울산 지정점울산시 남구 갈밭로 24, 2층 207호(삼산동, 공구월드)052-266-7673
113114에스케이네트웍스서비스전남순천시전라출장광양 지정점전라남도 광양시 광양읍 읍성2길 17, 1층061-742-1160~1
114115에스케이네트웍스서비스전남목포전라출장SG광주 지정점광주광역시 북구 우치로 14-1(중흥동)062-527-6797
115116에스케이네트웍스서비스전북전주전라출장/내방전주 지정점전라북도 전주시 덕진구 안덕원로 276(인후동1가) ,통신백화점내 Service N 전주센터063-278-7101
116117에스케이네트웍스서비스전북남원전라내방남원나우 지정점전라북도 남원시 시청로 67(향교동)063-625-0272
117118에스케이네트웍스서비스전북정읍,남원전라출장정읍 지정점전라북도 전주시 완산구 한절길 67-28, 제5동(효자동2가)010-****-****
118119에스케이네트웍스서비스제주노형동전라출장제주 지정점제주특별자치도 제주시 월광로 151(노형동)064-747-8257
119120에스케이네트웍스서비스충북청주충청출장청주 지정점충청북도 청주시 흥덕구 복대로 174, 3층(복대동)043-903-3266