Overview

Dataset statistics

Number of variables7
Number of observations1495
Missing cells809
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.3 KiB
Average record size in memory59.1 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description인천광역시 미추홀구의 미용업소 현황에 대한 데이터로 상호명, 도로명주소, 전화번호 등의 항목을 제공하고 있습니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15061013/fileData.do

Alerts

연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
전화번호 has 807 (54.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:36:59.510930
Analysis finished2024-04-29 22:37:03.206630
Duration3.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean748
Minimum1
Maximum1495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-30T07:37:03.276310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75.7
Q1374.5
median748
Q31121.5
95-th percentile1420.3
Maximum1495
Range1494
Interquartile range (IQR)747

Descriptive statistics

Standard deviation431.71364
Coefficient of variation (CV)0.57715727
Kurtosis-1.2
Mean748
Median Absolute Deviation (MAD)374
Skewness0
Sum1118260
Variance186376.67
MonotonicityStrictly increasing
2024-04-30T07:37:03.404634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
995 1
 
0.1%
1004 1
 
0.1%
1003 1
 
0.1%
1002 1
 
0.1%
1001 1
 
0.1%
1000 1
 
0.1%
999 1
 
0.1%
998 1
 
0.1%
997 1
 
0.1%
Other values (1485) 1485
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1495 1
0.1%
1494 1
0.1%
1493 1
0.1%
1492 1
0.1%
1491 1
0.1%
1490 1
0.1%
1489 1
0.1%
1488 1
0.1%
1487 1
0.1%
1486 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
일반미용업
811 
피부미용업
141 
미용업
140 
네일미용업
134 
종합미용업
 
54
Other values (10)
215 

Length

Max length23
Median length5
Mean length6.1043478
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 811
54.2%
피부미용업 141
 
9.4%
미용업 140
 
9.4%
네일미용업 134
 
9.0%
종합미용업 54
 
3.6%
화장ㆍ분장 미용업 53
 
3.5%
네일미용업, 화장ㆍ분장 미용업 48
 
3.2%
피부미용업, 네일미용업 46
 
3.1%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 18
 
1.2%
일반미용업, 네일미용업 13
 
0.9%
Other values (5) 37
 
2.5%

Length

2024-04-30T07:37:03.594941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 848
46.2%
미용업 292
 
15.9%
네일미용업 266
 
14.5%
피부미용업 224
 
12.2%
화장ㆍ분장 152
 
8.3%
종합미용업 54
 
2.9%

상호명
Text

Distinct1438
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-04-30T07:37:03.854660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length6.3919732
Min length2

Characters and Unicode

Total characters9556
Distinct characters599
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1387 ?
Unique (%)92.8%

Sample

1st row도원미장원
2nd row훈이미용실
3rd row이윤희헤어센스
4th row영미헤어두
5th row영헤어뱅크미용실
ValueCountFrequency (%)
헤어 33
 
1.7%
미용실 21
 
1.1%
네일 21
 
1.1%
hair 19
 
1.0%
nail 18
 
0.9%
헤어살롱 7
 
0.4%
주안점 7
 
0.4%
헤어샵 7
 
0.4%
살롱 6
 
0.3%
속눈썹 6
 
0.3%
Other values (1576) 1751
92.4%
2024-04-30T07:37:04.261014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 638
 
6.7%
헤 612
 
6.4%
401
 
4.2%
미 373
 
3.9%
용 269
 
2.8%
실 259
 
2.7%
일 211
 
2.2%
이 209
 
2.2%
네 206
 
2.2%
스 175
 
1.8%
Other values (589) 6203
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7766
81.3%
Lowercase Letter 578
 
6.0%
Space Separator 401
 
4.2%
Uppercase Letter 351
 
3.7%
Close Punctuation 154
 
1.6%
Open Punctuation 154
 
1.6%
Other Punctuation 88
 
0.9%
Decimal Number 49
 
0.5%
Dash Punctuation 7
 
0.1%
Math Symbol 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 638
 
8.2%
헤 612
 
7.9%
미 373
 
4.8%
용 269
 
3.5%
실 259
 
3.3%
일 211
 
2.7%
이 209
 
2.7%
네 206
 
2.7%
스 175
 
2.3%
리 168
 
2.2%
Other values (513) 4646
59.8%
Uppercase Letter
ValueCountFrequency (%)
A 44
12.5%
N 41
11.7%
H 27
 
7.7%
I 26
 
7.4%
S 22
 
6.3%
O 20
 
5.7%
E 19
 
5.4%
L 18
 
5.1%
R 17
 
4.8%
J 15
 
4.3%
Other values (16) 102
29.1%
Lowercase Letter
ValueCountFrequency (%)
a 82
14.2%
i 70
12.1%
e 58
10.0%
l 47
 
8.1%
n 43
 
7.4%
o 40
 
6.9%
r 37
 
6.4%
h 32
 
5.5%
s 26
 
4.5%
u 25
 
4.3%
Other values (13) 118
20.4%
Decimal Number
ValueCountFrequency (%)
1 11
22.4%
2 10
20.4%
0 9
18.4%
3 6
12.2%
5 5
10.2%
6 4
 
8.2%
4 1
 
2.0%
8 1
 
2.0%
7 1
 
2.0%
9 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 29
33.0%
& 20
22.7%
. 14
15.9%
# 10
 
11.4%
' 7
 
8.0%
: 4
 
4.5%
· 3
 
3.4%
; 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 2
40.0%
< 1
20.0%
= 1
20.0%
> 1
20.0%
Space Separator
ValueCountFrequency (%)
401
100.0%
Close Punctuation
ValueCountFrequency (%)
) 154
100.0%
Open Punctuation
ValueCountFrequency (%)
( 154
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7759
81.2%
Latin 929
 
9.7%
Common 861
 
9.0%
Han 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 638
 
8.2%
헤 612
 
7.9%
미 373
 
4.8%
용 269
 
3.5%
실 259
 
3.3%
일 211
 
2.7%
이 209
 
2.7%
네 206
 
2.7%
스 175
 
2.3%
리 168
 
2.2%
Other values (508) 4639
59.8%
Latin
ValueCountFrequency (%)
a 82
 
8.8%
i 70
 
7.5%
e 58
 
6.2%
l 47
 
5.1%
A 44
 
4.7%
n 43
 
4.6%
N 41
 
4.4%
o 40
 
4.3%
r 37
 
4.0%
h 32
 
3.4%
Other values (39) 435
46.8%
Common
ValueCountFrequency (%)
401
46.6%
) 154
 
17.9%
( 154
 
17.9%
, 29
 
3.4%
& 20
 
2.3%
. 14
 
1.6%
1 11
 
1.3%
2 10
 
1.2%
# 10
 
1.2%
0 9
 
1.0%
Other values (17) 49
 
5.7%
Han
ValueCountFrequency (%)
美 3
42.9%
家 1
 
14.3%
眞 1
 
14.3%
后 1
 
14.3%
秀 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7759
81.2%
ASCII 1787
 
18.7%
CJK 7
 
0.1%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 638
 
8.2%
헤 612
 
7.9%
미 373
 
4.8%
용 269
 
3.5%
실 259
 
3.3%
일 211
 
2.7%
이 209
 
2.7%
네 206
 
2.7%
스 175
 
2.3%
리 168
 
2.2%
Other values (508) 4639
59.8%
ASCII
ValueCountFrequency (%)
401
22.4%
) 154
 
8.6%
( 154
 
8.6%
a 82
 
4.6%
i 70
 
3.9%
e 58
 
3.2%
l 47
 
2.6%
A 44
 
2.5%
n 43
 
2.4%
N 41
 
2.3%
Other values (65) 693
38.8%
None
ValueCountFrequency (%)
· 3
100.0%
CJK
ValueCountFrequency (%)
美 3
42.9%
家 1
 
14.3%
眞 1
 
14.3%
后 1
 
14.3%
秀 1
 
14.3%

도로명주소
Text

Distinct1478
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-04-30T07:37:04.523978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length55
Mean length34.405351
Min length19

Characters and Unicode

Total characters51436
Distinct characters304
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1462 ?
Unique (%)97.8%

Sample

1st row인천광역시 미추홀구 석정로50번길 21-4 (숭의동)
2nd row인천광역시 미추홀구 남주길125번길 34 (주안동)
3rd row인천광역시 미추홀구 독정이로 118 (숭의동)
4th row인천광역시 미추홀구 능해길 38, B동 1층 102호 (용현동, 동아아파트 상가)
5th row인천광역시 미추홀구 경인로430번길 57 (주안동)
ValueCountFrequency (%)
인천광역시 1495
 
15.0%
미추홀구 1495
 
15.0%
주안동 639
 
6.4%
1층 591
 
5.9%
용현동 337
 
3.4%
2층 153
 
1.5%
학익동 144
 
1.4%
도화동 130
 
1.3%
숭의동 117
 
1.2%
경인로 111
 
1.1%
Other values (1507) 4746
47.7%
2024-04-30T07:37:04.976398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8478
 
16.5%
1 2181
 
4.2%
인 1875
 
3.6%
홀 1727
 
3.4%
동 1704
 
3.3%
미 1656
 
3.2%
추 1647
 
3.2%
역 1547
 
3.0%
시 1539
 
3.0%
천 1529
 
3.0%
Other values (294) 27553
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29733
57.8%
Space Separator 8478
 
16.5%
Decimal Number 8416
 
16.4%
Open Punctuation 1490
 
2.9%
Close Punctuation 1490
 
2.9%
Other Punctuation 1387
 
2.7%
Dash Punctuation 259
 
0.5%
Uppercase Letter 145
 
0.3%
Lowercase Letter 33
 
0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 1875
 
6.3%
홀 1727
 
5.8%
동 1704
 
5.7%
미 1656
 
5.6%
추 1647
 
5.5%
역 1547
 
5.2%
시 1539
 
5.2%
천 1529
 
5.1%
구 1503
 
5.1%
광 1502
 
5.1%
Other values (251) 13504
45.4%
Uppercase Letter
ValueCountFrequency (%)
B 43
29.7%
A 17
 
11.7%
S 16
 
11.0%
I 12
 
8.3%
K 10
 
6.9%
C 10
 
6.9%
E 9
 
6.2%
V 7
 
4.8%
W 7
 
4.8%
N 3
 
2.1%
Other values (6) 11
 
7.6%
Decimal Number
ValueCountFrequency (%)
1 2181
25.9%
2 1334
15.9%
3 926
11.0%
0 874
10.4%
4 685
 
8.1%
5 576
 
6.8%
8 503
 
6.0%
7 488
 
5.8%
6 472
 
5.6%
9 377
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
e 13
39.4%
k 7
21.2%
y 7
21.2%
h 1
 
3.0%
c 1
 
3.0%
s 1
 
3.0%
a 1
 
3.0%
p 1
 
3.0%
b 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 1383
99.7%
. 3
 
0.2%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
8478
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1490
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1490
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 259
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29733
57.8%
Common 21525
41.8%
Latin 178
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 1875
 
6.3%
홀 1727
 
5.8%
동 1704
 
5.7%
미 1656
 
5.6%
추 1647
 
5.5%
역 1547
 
5.2%
시 1539
 
5.2%
천 1529
 
5.1%
구 1503
 
5.1%
광 1502
 
5.1%
Other values (251) 13504
45.4%
Latin
ValueCountFrequency (%)
B 43
24.2%
A 17
 
9.6%
S 16
 
9.0%
e 13
 
7.3%
I 12
 
6.7%
K 10
 
5.6%
C 10
 
5.6%
E 9
 
5.1%
k 7
 
3.9%
V 7
 
3.9%
Other values (15) 34
19.1%
Common
ValueCountFrequency (%)
8478
39.4%
1 2181
 
10.1%
( 1490
 
6.9%
) 1490
 
6.9%
, 1383
 
6.4%
2 1334
 
6.2%
3 926
 
4.3%
0 874
 
4.1%
4 685
 
3.2%
5 576
 
2.7%
Other values (8) 2108
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29733
57.8%
ASCII 21703
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8478
39.1%
1 2181
 
10.0%
( 1490
 
6.9%
) 1490
 
6.9%
, 1383
 
6.4%
2 1334
 
6.1%
3 926
 
4.3%
0 874
 
4.0%
4 685
 
3.2%
5 576
 
2.7%
Other values (33) 2286
 
10.5%
Hangul
ValueCountFrequency (%)
인 1875
 
6.3%
홀 1727
 
5.8%
동 1704
 
5.7%
미 1656
 
5.6%
추 1647
 
5.5%
역 1547
 
5.2%
시 1539
 
5.2%
천 1529
 
5.1%
구 1503
 
5.1%
광 1502
 
5.1%
Other values (251) 13504
45.4%

전화번호
Text

MISSING 

Distinct685
Distinct (%)99.6%
Missing807
Missing (%)54.0%
Memory size11.8 KiB
2024-04-30T07:37:05.233960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.05814
Min length11

Characters and Unicode

Total characters8296
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique682 ?
Unique (%)99.1%

Sample

1st row032-866-1321
2nd row032-882-1424
3rd row032-888-0055
4th row032-866-6380
5th row032-873-4682
ValueCountFrequency (%)
032-868-9656 2
 
0.3%
032-822-2922 2
 
0.3%
032-888-9479 2
 
0.3%
032-421-1375 1
 
0.1%
032-549-0316 1
 
0.1%
032-223-4169 1
 
0.1%
032-861-5656 1
 
0.1%
032-881-4588 1
 
0.1%
032-873-9322 1
 
0.1%
032-861-8876 1
 
0.1%
Other values (675) 675
98.1%
2024-04-30T07:37:05.609767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1376
16.6%
2 1212
14.6%
0 1063
12.8%
3 1063
12.8%
8 911
11.0%
7 541
 
6.5%
4 528
 
6.4%
6 507
 
6.1%
5 406
 
4.9%
1 354
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6920
83.4%
Dash Punctuation 1376
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1212
17.5%
0 1063
15.4%
3 1063
15.4%
8 911
13.2%
7 541
7.8%
4 528
7.6%
6 507
7.3%
5 406
 
5.9%
1 354
 
5.1%
9 335
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 1376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1376
16.6%
2 1212
14.6%
0 1063
12.8%
3 1063
12.8%
8 911
11.0%
7 541
 
6.5%
4 528
 
6.4%
6 507
 
6.1%
5 406
 
4.9%
1 354
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1376
16.6%
2 1212
14.6%
0 1063
12.8%
3 1063
12.8%
8 911
11.0%
7 541
 
6.5%
4 528
 
6.4%
6 507
 
6.1%
5 406
 
4.9%
1 354
 
4.3%

위도
Real number (ℝ)

Distinct1142
Distinct (%)76.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean37.454963
Minimum37.435615
Maximum37.478322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-30T07:37:05.779839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.435615
5-th percentile37.439304
Q137.448044
median37.455592
Q337.461991
95-th percentile37.468159
Maximum37.478322
Range0.04270738
Interquartile range (IQR)0.01394687

Descriptive statistics

Standard deviation0.0089936675
Coefficient of variation (CV)0.00024011951
Kurtosis-0.69697448
Mean37.454963
Median Absolute Deviation (MAD)0.00715239
Skewness-0.03913222
Sum55957.715
Variance8.0886055 × 10-5
MonotonicityNot monotonic
2024-04-30T07:37:05.931839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4580182 43
 
2.9%
37.46440455 28
 
1.9%
37.44425946 27
 
1.8%
37.46366077 16
 
1.1%
37.45875167 14
 
0.9%
37.45788358 10
 
0.7%
37.44762897 10
 
0.7%
37.45201495 9
 
0.6%
37.45826008 9
 
0.6%
37.44760615 8
 
0.5%
Other values (1132) 1320
88.3%
ValueCountFrequency (%)
37.43561471 2
0.1%
37.43571272 1
0.1%
37.43582371 1
0.1%
37.43592988 1
0.1%
37.4361663 1
0.1%
37.43639219 1
0.1%
37.43661464 1
0.1%
37.43677906 1
0.1%
37.43694545 1
0.1%
37.43708799 1
0.1%
ValueCountFrequency (%)
37.47832209 1
 
0.1%
37.47761813 1
 
0.1%
37.47747322 1
 
0.1%
37.47745107 1
 
0.1%
37.47740771 1
 
0.1%
37.47728834 1
 
0.1%
37.47696529 3
0.2%
37.47693979 1
 
0.1%
37.47683065 1
 
0.1%
37.47613005 2
0.1%

경도
Real number (ℝ)

Distinct1140
Distinct (%)76.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean126.66816
Minimum126.63246
Maximum126.6985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-30T07:37:06.139866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63246
5-th percentile126.63839
Q1126.6542
median126.67385
Q3126.68129
95-th percentile126.69194
Maximum126.6985
Range0.0660431
Interquartile range (IQR)0.027083275

Descriptive statistics

Standard deviation0.017120759
Coefficient of variation (CV)0.00013516229
Kurtosis-1.0200641
Mean126.66816
Median Absolute Deviation (MAD)0.01186925
Skewness-0.39404597
Sum189242.23
Variance0.00029312037
MonotonicityNot monotonic
2024-04-30T07:37:06.299182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6811632 43
 
2.9%
126.6801618 28
 
1.9%
126.6524737 27
 
1.8%
126.6817327 16
 
1.1%
126.6775268 14
 
0.9%
126.6470946 10
 
0.7%
126.6902724 10
 
0.7%
126.6450629 9
 
0.6%
126.6426622 9
 
0.6%
126.6485366 8
 
0.5%
Other values (1130) 1320
88.3%
ValueCountFrequency (%)
126.6324617 1
0.1%
126.6327692 1
0.1%
126.6332704 1
0.1%
126.633278 1
0.1%
126.6337787 1
0.1%
126.633928 1
0.1%
126.6342989 1
0.1%
126.6343038 1
0.1%
126.6343524 1
0.1%
126.6343913 1
0.1%
ValueCountFrequency (%)
126.6985048 3
0.2%
126.698306 1
 
0.1%
126.6972576 1
 
0.1%
126.6966793 1
 
0.1%
126.6962602 2
0.1%
126.6962105 2
0.1%
126.6961002 1
 
0.1%
126.6956246 1
 
0.1%
126.6954868 1
 
0.1%
126.6954668 1
 
0.1%

Interactions

2024-04-30T07:37:02.524923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:01.693303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:02.153966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:02.626517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:01.890113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:02.292808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:02.744569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:02.006999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:02.405878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:37:06.413613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명위도경도
연번1.0000.9090.2310.224
업종명0.9091.0000.1580.186
위도0.2310.1581.0000.697
경도0.2240.1860.6971.000
2024-04-30T07:37:06.514540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도업종명
연번1.0000.098-0.0120.632
위도0.0981.0000.0000.059
경도-0.0120.0001.0000.070
업종명0.6320.0590.0701.000

Missing values

2024-04-30T07:37:02.892601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:37:03.038200image/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.
2024-04-30T07:37:03.150829image/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

연번업종명상호명도로명주소전화번호위도경도
01미용업도원미장원인천광역시 미추홀구 석정로50번길 21-4 (숭의동)<NA>37.463623126.645409
12미용업훈이미용실인천광역시 미추홀구 남주길125번길 34 (주안동)032-866-132137.455418126.676504
23미용업이윤희헤어센스인천광역시 미추홀구 독정이로 118 (숭의동)<NA>37.465867126.649649
34미용업영미헤어두인천광역시 미추홀구 능해길 38, B동 1층 102호 (용현동, 동아아파트 상가)032-882-142437.458857126.639598
45미용업영헤어뱅크미용실인천광역시 미추홀구 경인로430번길 57 (주안동)032-888-005537.455282126.6877
56미용업새한미용실인천광역시 미추홀구 남주길11번길 19 (주안동)032-866-638037.455167126.670454
67미용업명성헤어아트인천광역시 미추홀구 용현동 52-20 대신아트빌라2동 102호<NA>37.449937126.664501
78미용업레지나헤어모드미용실인천광역시 미추홀구 석정로 368 (주안동)032-873-468237.467072126.67641
89미용업프로헤어샾인천광역시 미추홀구 낙섬중로91번길 4-14 (용현동)<NA>37.456139126.640769
910미용업오미용실인천광역시 미추홀구 제일로40번길 23 (도화동)032-864-637237.457611126.674606
연번업종명상호명도로명주소전화번호위도경도
14851486피부미용업, 네일미용업, 화장ㆍ분장 미용업안녕 속눈썹인천광역시 미추홀구 승학길104번길 29, 2층 (주안동)<NA>37.444353126.68155
14861487피부미용업, 네일미용업, 화장ㆍ분장 미용업네일 102(Nail 102)인천광역시 미추홀구 숙골로 120, 1층 103호 (도화동)<NA>37.472606126.661565
14871488피부미용업, 네일미용업, 화장ㆍ분장 미용업샤샤네일인천광역시 미추홀구 염전로168번길 28, 도화두손지젤시티 C동 1층 106호 (도화동)<NA>37.476965126.659269
14881489피부미용업, 네일미용업, 화장ㆍ분장 미용업네일미니인천광역시 미추홀구 한나루로489번길 107, 1층 102호 (용현동)<NA>37.452197126.661419
14891490피부미용업, 네일미용업, 화장ㆍ분장 미용업반디인하우스인천광역시 미추홀구 숙골로87번길 5, 204동 2층 2-20, 21호 (도화동, 더샵 인천스카이타워 2단지)<NA>37.469475126.663262
14901491피부미용업, 네일미용업, 화장ㆍ분장 미용업부티스튜디오(부티studio)인천광역시 미추홀구 인하로91번길 71, 1층 일부호 (용현동)<NA>37.451144126.661762
14911492피부미용업, 네일미용업, 화장ㆍ분장 미용업모도리 네일(MODORI NAIL)인천광역시 미추홀구 독배로382번길 12, 1층 (용현동)<NA>37.453632126.649205
14921493피부미용업, 네일미용업, 화장ㆍ분장 미용업새봄뷰티인천광역시 미추홀구 경인로 372, 201동 2층 2031호 (주안동, 포레나 미추홀)<NA>37.458018126.681163
14931494피부미용업, 네일미용업, 화장ㆍ분장 미용업헬로네일인천광역시 미추홀구 주안로 지하 86, 주안역 지하도상가 지하1층 105호 (주안동)<NA>37.464405126.680162
14941495피부미용업, 네일미용업, 화장ㆍ분장 미용업랄랄왁싱(lallal WAXING)인천광역시 미추홀구 경원대로 895, 3층 (주안동)<NA>37.462633126.689479