Overview

Dataset statistics

Number of variables7
Number of observations728
Missing cells219
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.8 KiB
Average record size in memory60.2 B

Variable types

Categorical1
Text3
Numeric3

Dataset

Description경상북도 100인 이상 기업 현황입니다. 년도, 사업체명, 주소, 산업분류번호, 전화번호 등에 관한 정보입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15126828/fileData.do

Alerts

년도 has constant value ""Constant
응답자전화지역번호 has 73 (10.0%) missing valuesMissing
응답자전화국번호 has 73 (10.0%) missing valuesMissing
응답자전화일련번호 has 73 (10.0%) missing valuesMissing

Reproduction

Analysis started2024-03-15 00:17:47.251344
Analysis finished2024-03-15 00:17:51.696166
Duration4.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2022
728 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 728
100.0%

Length

2024-03-15T09:17:51.812922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:17:51.984620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 728
100.0%

사업체명
Text

Distinct725
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2024-03-15T09:17:52.738155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21
Mean length9.5700549
Min length2

Characters and Unicode

Total characters6967
Distinct characters436
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique722 ?
Unique (%)99.2%

Sample

1st row(주)삼광윈테크2
2nd row예천군청
3rd row의료법인 서명의료재단
4th row광성복지센타
5th rowkT텔레캅서비스(주)영남대학교출장소
ValueCountFrequency (%)
주식회사 54
 
5.6%
의료법인 13
 
1.4%
주 9
 
0.9%
경상북도 5
 
0.5%
김천공장 5
 
0.5%
주)이마트 4
 
0.4%
경주공장 3
 
0.3%
산학협력단 3
 
0.3%
3공장 3
 
0.3%
포항1공장 3
 
0.3%
Other values (822) 858
89.4%
2024-03-15T09:17:54.215844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 482
 
6.9%
( 353
 
5.1%
) 351
 
5.0%
231
 
3.3%
공 158
 
2.3%
장 142
 
2.0%
사 134
 
1.9%
스 133
 
1.9%
경 121
 
1.7%
이 110
 
1.6%
Other values (426) 4752
68.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5809
83.4%
Open Punctuation 374
 
5.4%
Close Punctuation 374
 
5.4%
Space Separator 233
 
3.3%
Uppercase Letter 67
 
1.0%
Decimal Number 58
 
0.8%
Lowercase Letter 36
 
0.5%
Other Punctuation 12
 
0.2%
Other Symbol 2
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 482
 
8.3%
공 158
 
2.7%
장 142
 
2.4%
사 134
 
2.3%
스 133
 
2.3%
경 121
 
2.1%
이 110
 
1.9%
원 106
 
1.8%
구 91
 
1.6%
포 90
 
1.5%
Other values (372) 4242
73.0%
Uppercase Letter
ValueCountFrequency (%)
S 16
23.9%
C 7
10.4%
T 6
 
9.0%
K 6
 
9.0%
I 5
 
7.5%
A 5
 
7.5%
M 4
 
6.0%
D 2
 
3.0%
B 2
 
3.0%
L 2
 
3.0%
Other values (10) 12
17.9%
Lowercase Letter
ValueCountFrequency (%)
o 6
16.7%
l 5
13.9%
e 4
11.1%
c 3
8.3%
k 2
 
5.6%
i 2
 
5.6%
a 2
 
5.6%
g 2
 
5.6%
h 2
 
5.6%
p 2
 
5.6%
Other values (6) 6
16.7%
Decimal Number
ValueCountFrequency (%)
2 24
41.4%
1 19
32.8%
3 9
 
15.5%
4 3
 
5.2%
5 2
 
3.4%
6 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
* 6
50.0%
, 3
25.0%
& 2
 
16.7%
/ 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 353
94.4%
（ 21
 
5.6%
Close Punctuation
ValueCountFrequency (%)
) 351
93.9%
） 23
 
6.1%
Space Separator
ValueCountFrequency (%)
231
99.1%
　 2
 
0.9%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5811
83.4%
Common 1053
 
15.1%
Latin 103
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 482
 
8.3%
공 158
 
2.7%
장 142
 
2.4%
사 134
 
2.3%
스 133
 
2.3%
경 121
 
2.1%
이 110
 
1.9%
원 106
 
1.8%
구 91
 
1.6%
포 90
 
1.5%
Other values (373) 4244
73.0%
Latin
ValueCountFrequency (%)
S 16
15.5%
C 7
 
6.8%
T 6
 
5.8%
o 6
 
5.8%
K 6
 
5.8%
l 5
 
4.9%
I 5
 
4.9%
A 5
 
4.9%
e 4
 
3.9%
M 4
 
3.9%
Other values (26) 39
37.9%
Common
ValueCountFrequency (%)
( 353
33.5%
) 351
33.3%
231
21.9%
2 24
 
2.3%
） 23
 
2.2%
（ 21
 
2.0%
1 19
 
1.8%
3 9
 
0.9%
* 6
 
0.6%
4 3
 
0.3%
Other values (7) 13
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5809
83.4%
ASCII 1110
 
15.9%
None 48
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 482
 
8.3%
공 158
 
2.7%
장 142
 
2.4%
사 134
 
2.3%
스 133
 
2.3%
경 121
 
2.1%
이 110
 
1.9%
원 106
 
1.8%
구 91
 
1.6%
포 90
 
1.5%
Other values (372) 4242
73.0%
ASCII
ValueCountFrequency (%)
( 353
31.8%
) 351
31.6%
231
20.8%
2 24
 
2.2%
1 19
 
1.7%
S 16
 
1.4%
3 9
 
0.8%
C 7
 
0.6%
T 6
 
0.5%
* 6
 
0.5%
Other values (40) 88
 
7.9%
None
ValueCountFrequency (%)
） 23
47.9%
（ 21
43.8%
㈜ 2
 
4.2%
　 2
 
4.2%

지번주소
Text

Distinct683
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2024-03-15T09:17:55.698518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length27.200549
Min length16

Characters and Unicode

Total characters19802
Distinct characters336
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

Unique667 ?
Unique (%)91.6%

Sample

1st row경상북도 경산시 진량 신상 1209 6 A
2nd row경상북도 예천군
3rd row경상북도 경산시 중방 848 4
4th row경상북도 포항시 북구 창포동 613 두호상가
5th row경상북도 경산시 대동 214 1 7 4 영남대학교 본부
ValueCountFrequency (%)
경상북도 728
 
18.1%
포항시 162
 
4.0%
구미시 141
 
3.5%
1 120
 
3.0%
남구 107
 
2.7%
경산시 90
 
2.2%
경주시 82
 
2.0%
북구 55
 
1.4%
5 51
 
1.3%
2 50
 
1.2%
Other values (1167) 2428
60.5%
2024-03-15T09:17:57.639609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8191
41.4%
경 923
 
4.7%
북 809
 
4.1%
상 773
 
3.9%
도 768
 
3.9%
시 656
 
3.3%
1 533
 
2.7%
동 377
 
1.9%
구 344
 
1.7%
0 313
 
1.6%
Other values (326) 6115
30.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8840
44.6%
Space Separator 8191
41.4%
Decimal Number 2605
 
13.2%
Uppercase Letter 108
 
0.5%
Close Punctuation 21
 
0.1%
Open Punctuation 21
 
0.1%
Dash Punctuation 6
 
< 0.1%
Lowercase Letter 6
 
< 0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 923
 
10.4%
북 809
 
9.2%
상 773
 
8.7%
도 768
 
8.7%
시 656
 
7.4%
동 377
 
4.3%
구 344
 
3.9%
포 231
 
2.6%
산 225
 
2.5%
항 180
 
2.0%
Other values (288) 3554
40.2%
Uppercase Letter
ValueCountFrequency (%)
B 35
32.4%
A 30
27.8%
L 7
 
6.5%
S 6
 
5.6%
C 6
 
5.6%
G 5
 
4.6%
D 5
 
4.6%
H 3
 
2.8%
E 3
 
2.8%
M 2
 
1.9%
Other values (5) 6
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 533
20.5%
0 313
12.0%
2 301
11.6%
4 264
10.1%
3 259
9.9%
5 220
8.4%
6 206
 
7.9%
7 193
 
7.4%
9 162
 
6.2%
8 154
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
l 1
16.7%
a 1
16.7%
y 1
16.7%
p 1
16.7%
s 1
16.7%
i 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
/ 1
25.0%
, 1
25.0%
Space Separator
ValueCountFrequency (%)
8191
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10848
54.8%
Hangul 8840
44.6%
Latin 114
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 923
 
10.4%
북 809
 
9.2%
상 773
 
8.7%
도 768
 
8.7%
시 656
 
7.4%
동 377
 
4.3%
구 344
 
3.9%
포 231
 
2.6%
산 225
 
2.5%
항 180
 
2.0%
Other values (288) 3554
40.2%
Latin
ValueCountFrequency (%)
B 35
30.7%
A 30
26.3%
L 7
 
6.1%
S 6
 
5.3%
C 6
 
5.3%
G 5
 
4.4%
D 5
 
4.4%
H 3
 
2.6%
E 3
 
2.6%
M 2
 
1.8%
Other values (11) 12
 
10.5%
Common
ValueCountFrequency (%)
8191
75.5%
1 533
 
4.9%
0 313
 
2.9%
2 301
 
2.8%
4 264
 
2.4%
3 259
 
2.4%
5 220
 
2.0%
6 206
 
1.9%
7 193
 
1.8%
9 162
 
1.5%
Other values (7) 206
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10962
55.4%
Hangul 8840
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8191
74.7%
1 533
 
4.9%
0 313
 
2.9%
2 301
 
2.7%
4 264
 
2.4%
3 259
 
2.4%
5 220
 
2.0%
6 206
 
1.9%
7 193
 
1.8%
9 162
 
1.5%
Other values (28) 320
 
2.9%
Hangul
ValueCountFrequency (%)
경 923
 
10.4%
북 809
 
9.2%
상 773
 
8.7%
도 768
 
8.7%
시 656
 
7.4%
동 377
 
4.3%
구 344
 
3.9%
포 231
 
2.6%
산 225
 
2.5%
항 180
 
2.0%
Other values (288) 3554
40.2%

도로명주소
Text

Distinct657
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2024-03-15T09:17:58.780757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length10.043956
Min length7

Characters and Unicode

Total characters7312
Distinct characters216
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique627 ?
Unique (%)86.1%

Sample

1st row공단4로5길 19 0
2nd row충효로 111 0
3rd row경안로 208 0
4th row창흥로 46 0
5th row대학로 280 0
ValueCountFrequency (%)
0 605
27.7%
동해안로 46
 
2.1%
6262 33
 
1.5%
공단로 16
 
0.7%
13 16
 
0.7%
1 14
 
0.6%
10 13
 
0.6%
19 13
 
0.6%
9 12
 
0.6%
11 12
 
0.6%
Other values (717) 1401
64.2%
2024-03-15T09:18:00.383577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1456
19.9%
0 770
 
10.5%
로 588
 
8.0%
1 501
 
6.9%
2 394
 
5.4%
3 281
 
3.8%
6 243
 
3.3%
길 234
 
3.2%
5 219
 
3.0%
4 205
 
2.8%
Other values (206) 2421
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3082
42.1%
Other Letter 2774
37.9%
Space Separator 1456
19.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 588
21.2%
길 234
 
8.4%
단 202
 
7.3%
공 143
 
5.2%
대 109
 
3.9%
산 93
 
3.4%
동 87
 
3.1%
안 65
 
2.3%
해 55
 
2.0%
번 42
 
1.5%
Other values (195) 1156
41.7%
Decimal Number
ValueCountFrequency (%)
0 770
25.0%
1 501
16.3%
2 394
12.8%
3 281
 
9.1%
6 243
 
7.9%
5 219
 
7.1%
4 205
 
6.7%
7 166
 
5.4%
9 158
 
5.1%
8 145
 
4.7%
Space Separator
ValueCountFrequency (%)
1456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4538
62.1%
Hangul 2774
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 588
21.2%
길 234
 
8.4%
단 202
 
7.3%
공 143
 
5.2%
대 109
 
3.9%
산 93
 
3.4%
동 87
 
3.1%
안 65
 
2.3%
해 55
 
2.0%
번 42
 
1.5%
Other values (195) 1156
41.7%
Common
ValueCountFrequency (%)
1456
32.1%
0 770
17.0%
1 501
 
11.0%
2 394
 
8.7%
3 281
 
6.2%
6 243
 
5.4%
5 219
 
4.8%
4 205
 
4.5%
7 166
 
3.7%
9 158
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4538
62.1%
Hangul 2774
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1456
32.1%
0 770
17.0%
1 501
 
11.0%
2 394
 
8.7%
3 281
 
6.2%
6 243
 
5.4%
5 219
 
4.8%
4 205
 
4.5%
7 166
 
3.7%
9 158
 
3.5%
Hangul
ValueCountFrequency (%)
로 588
21.2%
길 234
 
8.4%
단 202
 
7.3%
공 143
 
5.2%
대 109
 
3.9%
산 93
 
3.4%
동 87
 
3.1%
안 65
 
2.3%
해 55
 
2.0%
번 42
 
1.5%
Other values (195) 1156
41.7%

응답자전화지역번호
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)1.4%
Missing73
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean53.389313
Minimum2
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-03-15T09:18:00.669293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile53
Q154
median54
Q354
95-th percentile54
Maximum70
Range68
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5227809
Coefficient of variation (CV)0.10344357
Kurtosis58.705241
Mean53.389313
Median Absolute Deviation (MAD)0
Skewness-6.4457431
Sum34970
Variance30.501109
MonotonicityNot monotonic
2024-03-15T09:18:01.006898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
54 538
73.9%
53 90
 
12.4%
70 10
 
1.4%
31 5
 
0.7%
2 5
 
0.7%
41 3
 
0.4%
43 2
 
0.3%
32 1
 
0.1%
42 1
 
0.1%
(Missing) 73
 
10.0%
ValueCountFrequency (%)
2 5
 
0.7%
31 5
 
0.7%
32 1
 
0.1%
41 3
 
0.4%
42 1
 
0.1%
43 2
 
0.3%
53 90
 
12.4%
54 538
73.9%
70 10
 
1.4%
ValueCountFrequency (%)
70 10
 
1.4%
54 538
73.9%
53 90
 
12.4%
43 2
 
0.3%
42 1
 
0.1%
41 3
 
0.4%
32 1
 
0.1%
31 5
 
0.7%
2 5
 
0.7%

응답자전화국번호
Real number (ℝ)

MISSING 

Distinct225
Distinct (%)34.4%
Missing73
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean692.76183
Minimum220
Maximum8894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-03-15T09:18:01.419309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile260
Q1353.5
median539
Q3789
95-th percentile970.3
Maximum8894
Range8674
Interquartile range (IQR)435.5

Descriptive statistics

Standard deviation906.04544
Coefficient of variation (CV)1.3078744
Kurtosis54.474493
Mean692.76183
Median Absolute Deviation (MAD)231
Skewness7.0669932
Sum453759
Variance820918.33
MonotonicityNot monotonic
2024-03-15T09:18:01.777855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 20
 
2.7%
859 16
 
2.2%
770 16
 
2.2%
470 14
 
1.9%
420 11
 
1.5%
479 11
 
1.5%
639 11
 
1.5%
288 10
 
1.4%
271 10
 
1.4%
468 10
 
1.4%
Other values (215) 526
72.3%
(Missing) 73
 
10.0%
ValueCountFrequency (%)
220 2
0.3%
221 1
 
0.1%
223 2
0.3%
230 3
0.4%
231 1
 
0.1%
232 1
 
0.1%
240 3
0.4%
241 2
0.3%
242 1
 
0.1%
244 1
 
0.1%
ValueCountFrequency (%)
8894 1
0.1%
8873 1
0.1%
8648 1
0.1%
8067 1
0.1%
7878 1
0.1%
7713 1
0.1%
7100 1
0.1%
7094 1
0.1%
4946 1
0.1%
4165 1
0.1%

응답자전화일련번호
Real number (ℝ)

MISSING 

Distinct587
Distinct (%)89.6%
Missing73
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4563.9756
Minimum2
Maximum9998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-03-15T09:18:02.093924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile224.5
Q12011.5
median4415
Q37127
95-th percentile9145.1
Maximum9998
Range9996
Interquartile range (IQR)5115.5

Descriptive statistics

Standard deviation2914.356
Coefficient of variation (CV)0.63855643
Kurtosis-1.2626881
Mean4563.9756
Median Absolute Deviation (MAD)2597
Skewness0.10475114
Sum2989404
Variance8493470.7
MonotonicityNot monotonic
2024-03-15T09:18:02.487486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331 3
 
0.4%
114 3
 
0.4%
5811 3
 
0.4%
8812 3
 
0.4%
113 3
 
0.4%
6451 3
 
0.4%
119 2
 
0.3%
3313 2
 
0.3%
5188 2
 
0.3%
1252 2
 
0.3%
Other values (577) 629
86.4%
(Missing) 73
 
10.0%
ValueCountFrequency (%)
2 1
 
0.1%
9 1
 
0.1%
11 2
0.3%
12 1
 
0.1%
31 1
 
0.1%
46 1
 
0.1%
61 1
 
0.1%
100 2
0.3%
110 1
 
0.1%
113 3
0.4%
ValueCountFrequency (%)
9998 1
0.1%
9933 1
0.1%
9920 1
0.1%
9913 1
0.1%
9902 1
0.1%
9900 1
0.1%
9862 1
0.1%
9814 1
0.1%
9766 1
0.1%
9736 1
0.1%

Interactions

2024-03-15T09:17:49.937662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:48.160910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:48.948263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:50.212251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:48.433825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:49.435128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:50.463321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:48.689714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:17:49.680107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:18:02.725536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자전화지역번호응답자전화국번호응답자전화일련번호
응답자전화지역번호1.0000.7640.111
응답자전화국번호0.7641.0000.000
응답자전화일련번호0.1110.0001.000
2024-03-15T09:18:02.976520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자전화지역번호응답자전화국번호응답자전화일련번호
응답자전화지역번호1.000-0.2810.051
응답자전화국번호-0.2811.0000.008
응답자전화일련번호0.0510.0081.000

Missing values

2024-03-15T09:17:50.811571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:17:51.214498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-15T09:17:51.594046image/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

년도사업체명지번주소도로명주소응답자전화지역번호응답자전화국번호응답자전화일련번호
02022(주)삼광윈테크2경상북도 경산시 진량 신상 1209 6 A공단4로5길 19 0538561085
12022예천군청경상북도 예천군충효로 111 0546506843
22022의료법인 서명의료재단경상북도 경산시 중방 848 4경안로 208 0538198802
32022광성복지센타경상북도 포항시 북구 창포동 613 두호상가창흥로 46 0542491004
42022kT텔레캅서비스(주)영남대학교출장소경상북도 경산시 대동 214 1 7 4 영남대학교 본부대학로 280 0538103112
52022한국도로공사상주지사경상북도 상주시 헌신 313 1 51영남제일로 1234 0547116712
62022한화시스템(주)경상북도 구미시 공단동 259 사서함50호1공단로 244 0544608816
72022국립종자원경상북도 김천시 율곡 6 43혁신8로 119 054912114
82022(주)경원전자경상북도 칠곡군 가산 천평 280경북대로 1511 1549711533
92022(주)한금포항공장경상북도 포항시 남구 장흥동 1874철강산단로 240 0537643234
년도사업체명지번주소도로명주소응답자전화지역번호응답자전화국번호응답자전화일련번호
7182022주식회사 경신 경주공장경상북도 경주시 강동 오금 산11오금큰길 60 18547603300
7192022(의료법인)일원의료재단 늘푸른요양병원경상북도 경주시 용강 1276 10유림로13번길 155 0547492
7202022구미소방서경상북도 구미시 공단동 207수출대로 112 054440114
7212022위덕대학교경상북도 경주시 강동 유금 525 유마관동해대로 261 0547601035
7222022대교초등학교경상북도 칠곡군 석적 남율 880서중리7길 15 0549772432
7232022퓨릿경상북도 경주시 안강 두류리 865두류길 249 278<NA><NA><NA>
7242022(주)아라기술경상북도 포항시 남구 상도동 625 6 2상도로 42 0313456300
7252022홈플러스(주)안동점경상북도 안동시 운흥 247 58 홈플러스 2경동로 656 0548408300
7262022동국대학교 WISE캠퍼스경상북도 경주시 석장동 707 백주년기념관 G 2동대로 123 0547702062
7272022안동대학교경상북도 안동시 송천 388 대학교본부 다경동로 1375 0548207114