Overview

Dataset statistics

Number of variables7
Number of observations1387
Missing cells698
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.0 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://data.incheon.go.kr/findData/publicDataDetail?dataId=15061013&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
전화번호 has 697 (50.3%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-05-03 19:41:10.242028
Analysis finished2024-05-03 19:41:15.808960
Duration5.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean694
Minimum1
Maximum1387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2024-05-03T19:41:16.126787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70.3
Q1347.5
median694
Q31040.5
95-th percentile1317.7
Maximum1387
Range1386
Interquartile range (IQR)693

Descriptive statistics

Standard deviation400.53672
Coefficient of variation (CV)0.57714225
Kurtosis-1.2
Mean694
Median Absolute Deviation (MAD)347
Skewness0
Sum962578
Variance160429.67
MonotonicityStrictly increasing
2024-05-03T19:41:16.705191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
923 1
 
0.1%
931 1
 
0.1%
930 1
 
0.1%
929 1
 
0.1%
928 1
 
0.1%
927 1
 
0.1%
926 1
 
0.1%
925 1
 
0.1%
924 1
 
0.1%
Other values (1377) 1377
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 (%)
1387 1
0.1%
1386 1
0.1%
1385 1
0.1%
1384 1
0.1%
1383 1
0.1%
1382 1
0.1%
1381 1
0.1%
1380 1
0.1%
1379 1
0.1%
1378 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
일반미용업
763 
네일미용업
133 
피부미용업
127 
미용업
122 
종합미용업
 
54
Other values (11)
188 

Length

Max length23
Median length5
Mean length6.0958904
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 763
55.0%
네일미용업 133
 
9.6%
피부미용업 127
 
9.2%
미용업 122
 
8.8%
종합미용업 54
 
3.9%
피부미용업, 네일미용업 47
 
3.4%
네일미용업, 화장,분장 미용업 41
 
3.0%
화장,분장 미용업 34
 
2.5%
피부미용업, 네일미용업, 화장,분장 미용업 17
 
1.2%
일반미용업, 화장,분장 미용업 13
 
0.9%
Other values (6) 36
 
2.6%

Length

2024-05-03T19:41:17.504954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 799
47.2%
네일미용업 257
 
15.2%
미용업 248
 
14.6%
피부미용업 209
 
12.3%
화장,분장 126
 
7.4%
종합미용업 54
 
3.2%

상호명
Text

Distinct1328
Distinct (%)95.8%
Missing1
Missing (%)0.1%
Memory size11.0 KiB
2024-05-03T19:41:18.319411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length6.3354978
Min length2

Characters and Unicode

Total characters8781
Distinct characters584
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

Unique1275 ?
Unique (%)92.0%

Sample

1st row훈이미용실
2nd row영미헤어두
3rd row영헤어뱅크미용실
4th row새한미용실
5th row레지나헤어모드미용실
ValueCountFrequency (%)
헤어 31
 
1.8%
네일 24
 
1.4%
미용실 22
 
1.2%
hair 17
 
1.0%
nail 17
 
1.0%
살롱 7
 
0.4%
헤어살롱 7
 
0.4%
헤어샵 7
 
0.4%
더 7
 
0.4%
에스테틱 6
 
0.3%
Other values (1466) 1621
91.8%
2024-05-03T19:41:19.767320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 593
 
6.8%
헤 570
 
6.5%
380
 
4.3%
미 349
 
4.0%
용 254
 
2.9%
실 247
 
2.8%
일 214
 
2.4%
네 209
 
2.4%
이 197
 
2.2%
스 158
 
1.8%
Other values (574) 5610
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7089
80.7%
Lowercase Letter 546
 
6.2%
Space Separator 380
 
4.3%
Uppercase Letter 321
 
3.7%
Open Punctuation 151
 
1.7%
Close Punctuation 151
 
1.7%
Other Punctuation 81
 
0.9%
Decimal Number 49
 
0.6%
Dash Punctuation 7
 
0.1%
Connector Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 593
 
8.4%
헤 570
 
8.0%
미 349
 
4.9%
용 254
 
3.6%
실 247
 
3.5%
일 214
 
3.0%
네 209
 
2.9%
이 197
 
2.8%
스 158
 
2.2%
리 147
 
2.1%
Other values (501) 4151
58.6%
Uppercase Letter
ValueCountFrequency (%)
A 47
14.6%
N 44
13.7%
I 22
 
6.9%
E 19
 
5.9%
L 19
 
5.9%
H 18
 
5.6%
S 18
 
5.6%
O 17
 
5.3%
J 14
 
4.4%
D 13
 
4.0%
Other values (16) 90
28.0%
Lowercase Letter
ValueCountFrequency (%)
a 77
14.1%
i 60
11.0%
e 58
10.6%
n 44
 
8.1%
l 42
 
7.7%
o 38
 
7.0%
h 36
 
6.6%
r 28
 
5.1%
y 25
 
4.6%
s 23
 
4.2%
Other values (12) 115
21.1%
Other Punctuation
ValueCountFrequency (%)
, 23
28.4%
& 19
23.5%
. 16
19.8%
# 10
12.3%
' 5
 
6.2%
· 3
 
3.7%
; 2
 
2.5%
: 2
 
2.5%
? 1
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 13
26.5%
2 9
18.4%
0 8
16.3%
3 7
14.3%
5 4
 
8.2%
8 3
 
6.1%
6 3
 
6.1%
4 1
 
2.0%
7 1
 
2.0%
Math Symbol
ValueCountFrequency (%)
+ 2
66.7%
= 1
33.3%
Space Separator
ValueCountFrequency (%)
380
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7081
80.6%
Latin 867
 
9.9%
Common 825
 
9.4%
Han 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 593
 
8.4%
헤 570
 
8.0%
미 349
 
4.9%
용 254
 
3.6%
실 247
 
3.5%
일 214
 
3.0%
네 209
 
3.0%
이 197
 
2.8%
스 158
 
2.2%
리 147
 
2.1%
Other values (496) 4143
58.5%
Latin
ValueCountFrequency (%)
a 77
 
8.9%
i 60
 
6.9%
e 58
 
6.7%
A 47
 
5.4%
N 44
 
5.1%
n 44
 
5.1%
l 42
 
4.8%
o 38
 
4.4%
h 36
 
4.2%
r 28
 
3.2%
Other values (38) 393
45.3%
Common
ValueCountFrequency (%)
380
46.1%
( 151
 
18.3%
) 151
 
18.3%
, 23
 
2.8%
& 19
 
2.3%
. 16
 
1.9%
1 13
 
1.6%
# 10
 
1.2%
2 9
 
1.1%
0 8
 
1.0%
Other values (15) 45
 
5.5%
Han
ValueCountFrequency (%)
美 4
50.0%
后 1
 
12.5%
秀 1
 
12.5%
家 1
 
12.5%
眞 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7081
80.6%
ASCII 1689
 
19.2%
CJK 8
 
0.1%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 593
 
8.4%
헤 570
 
8.0%
미 349
 
4.9%
용 254
 
3.6%
실 247
 
3.5%
일 214
 
3.0%
네 209
 
3.0%
이 197
 
2.8%
스 158
 
2.2%
리 147
 
2.1%
Other values (496) 4143
58.5%
ASCII
ValueCountFrequency (%)
380
22.5%
( 151
 
8.9%
) 151
 
8.9%
a 77
 
4.6%
i 60
 
3.6%
e 58
 
3.4%
A 47
 
2.8%
N 44
 
2.6%
n 44
 
2.6%
l 42
 
2.5%
Other values (62) 635
37.6%
CJK
ValueCountFrequency (%)
美 4
50.0%
后 1
 
12.5%
秀 1
 
12.5%
家 1
 
12.5%
眞 1
 
12.5%
None
ValueCountFrequency (%)
· 3
100.0%

도로명주소
Text

Distinct1362
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2024-05-03T19:41:20.721619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length55
Mean length33.644557
Min length19

Characters and Unicode

Total characters46665
Distinct characters284
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

Unique1337 ?
Unique (%)96.4%

Sample

1st row인천광역시 미추홀구 남주길125번길 34 (주안동)
2nd row인천광역시 미추홀구 능해길 38, B동 1층 102호 (용현동, 동아아파트 상가)
3rd row인천광역시 미추홀구 경인로430번길 57 (주안동)
4th row인천광역시 미추홀구 남주길11번길 19 (주안동)
5th row인천광역시 미추홀구 석정로 368 (주안동)
ValueCountFrequency (%)
인천광역시 1387
 
15.4%
미추홀구 1387
 
15.4%
주안동 584
 
6.5%
1층 542
 
6.0%
용현동 315
 
3.5%
도화동 129
 
1.4%
학익동 121
 
1.3%
숭의동 114
 
1.3%
2층 112
 
1.2%
경인로 90
 
1.0%
Other values (1374) 4237
47.0%
2024-05-03T19:41:22.219097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7636
 
16.4%
1 1923
 
4.1%
인 1739
 
3.7%
홀 1567
 
3.4%
동 1559
 
3.3%
미 1509
 
3.2%
추 1501
 
3.2%
역 1439
 
3.1%
천 1419
 
3.0%
시 1418
 
3.0%
Other values (274) 24955
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27158
58.2%
Space Separator 7636
 
16.4%
Decimal Number 7447
 
16.0%
Open Punctuation 1392
 
3.0%
Close Punctuation 1392
 
3.0%
Other Punctuation 1238
 
2.7%
Dash Punctuation 235
 
0.5%
Uppercase Letter 130
 
0.3%
Lowercase Letter 34
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 1739
 
6.4%
홀 1567
 
5.8%
동 1559
 
5.7%
미 1509
 
5.6%
추 1501
 
5.5%
역 1439
 
5.3%
천 1419
 
5.2%
시 1418
 
5.2%
광 1394
 
5.1%
구 1393
 
5.1%
Other values (232) 12220
45.0%
Uppercase Letter
ValueCountFrequency (%)
B 25
19.2%
A 17
13.1%
S 16
12.3%
C 12
9.2%
I 12
9.2%
K 10
 
7.7%
E 10
 
7.7%
W 7
 
5.4%
V 7
 
5.4%
P 4
 
3.1%
Other values (5) 10
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 1923
25.8%
2 1095
14.7%
3 853
11.5%
0 711
 
9.5%
4 636
 
8.5%
5 555
 
7.5%
8 464
 
6.2%
6 428
 
5.7%
7 424
 
5.7%
9 358
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
e 13
38.2%
y 7
20.6%
k 7
20.6%
c 2
 
5.9%
a 1
 
2.9%
p 1
 
2.9%
s 1
 
2.9%
h 1
 
2.9%
b 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 1236
99.8%
. 1
 
0.1%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
7636
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1392
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 235
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27158
58.2%
Common 19343
41.5%
Latin 164
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 1739
 
6.4%
홀 1567
 
5.8%
동 1559
 
5.7%
미 1509
 
5.6%
추 1501
 
5.5%
역 1439
 
5.3%
천 1419
 
5.2%
시 1418
 
5.2%
광 1394
 
5.1%
구 1393
 
5.1%
Other values (232) 12220
45.0%
Latin
ValueCountFrequency (%)
B 25
15.2%
A 17
10.4%
S 16
9.8%
e 13
 
7.9%
C 12
 
7.3%
I 12
 
7.3%
K 10
 
6.1%
E 10
 
6.1%
W 7
 
4.3%
V 7
 
4.3%
Other values (14) 35
21.3%
Common
ValueCountFrequency (%)
7636
39.5%
1 1923
 
9.9%
( 1392
 
7.2%
) 1392
 
7.2%
, 1236
 
6.4%
2 1095
 
5.7%
3 853
 
4.4%
0 711
 
3.7%
4 636
 
3.3%
5 555
 
2.9%
Other values (8) 1914
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27158
58.2%
ASCII 19507
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7636
39.1%
1 1923
 
9.9%
( 1392
 
7.1%
) 1392
 
7.1%
, 1236
 
6.3%
2 1095
 
5.6%
3 853
 
4.4%
0 711
 
3.6%
4 636
 
3.3%
5 555
 
2.8%
Other values (32) 2078
 
10.7%
Hangul
ValueCountFrequency (%)
인 1739
 
6.4%
홀 1567
 
5.8%
동 1559
 
5.7%
미 1509
 
5.6%
추 1501
 
5.5%
역 1439
 
5.3%
천 1419
 
5.2%
시 1418
 
5.2%
광 1394
 
5.1%
구 1393
 
5.1%
Other values (232) 12220
45.0%

전화번호
Text

MISSING 

Distinct686
Distinct (%)99.4%
Missing697
Missing (%)50.3%
Memory size11.0 KiB
2024-05-03T19:41:22.883448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.053623
Min length11

Characters and Unicode

Total characters8317
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 (%)98.8%

Sample

1st row032-866-1321
2nd row032-882-1424
3rd row032-888-0055
4th row032-866-6380
5th row032-873-4682
ValueCountFrequency (%)
032-888-9479 2
 
0.3%
032-876-2203 2
 
0.3%
032-868-9656 2
 
0.3%
032-822-2922 2
 
0.3%
032-426-7923 1
 
0.1%
032-888-0043 1
 
0.1%
032-873-9322 1
 
0.1%
032-884-5730 1
 
0.1%
032-881-4588 1
 
0.1%
032-861-1911 1
 
0.1%
Other values (676) 676
98.0%
2024-05-03T19:41:24.142668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1380
16.6%
2 1228
14.8%
0 1059
12.7%
3 1055
12.7%
8 913
11.0%
7 546
 
6.6%
4 540
 
6.5%
6 509
 
6.1%
5 395
 
4.7%
1 355
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6937
83.4%
Dash Punctuation 1380
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1228
17.7%
0 1059
15.3%
3 1055
15.2%
8 913
13.2%
7 546
7.9%
4 540
7.8%
6 509
7.3%
5 395
 
5.7%
1 355
 
5.1%
9 337
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 1380
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8317
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1380
16.6%
2 1228
14.8%
0 1059
12.7%
3 1055
12.7%
8 913
11.0%
7 546
 
6.6%
4 540
 
6.5%
6 509
 
6.1%
5 395
 
4.7%
1 355
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1380
16.6%
2 1228
14.8%
0 1059
12.7%
3 1055
12.7%
8 913
11.0%
7 546
 
6.6%
4 540
 
6.5%
6 509
 
6.1%
5 395
 
4.7%
1 355
 
4.3%

위도
Real number (ℝ)

Distinct1093
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.455172
Minimum37.435824
Maximum37.478322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2024-05-03T19:41:24.638972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.435824
5-th percentile37.439304
Q137.448212
median37.455706
Q337.4623
95-th percentile37.468428
Maximum37.478322
Range0.04249838
Interquartile range (IQR)0.0140886

Descriptive statistics

Standard deviation0.0090769228
Coefficient of variation (CV)0.00024234097
Kurtosis-0.69339401
Mean37.455172
Median Absolute Deviation (MAD)0.00716726
Skewness-0.026963822
Sum51950.323
Variance8.2390528 × 10-5
MonotonicityNot monotonic
2024-05-03T19:41:25.266883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.46440455 29
 
2.1%
37.45897465 21
 
1.5%
37.44425946 14
 
1.0%
37.46366077 12
 
0.9%
37.4580182 12
 
0.9%
37.44762897 10
 
0.7%
37.45788358 10
 
0.7%
37.45201495 9
 
0.6%
37.46693482 9
 
0.6%
37.44756775 8
 
0.6%
Other values (1083) 1253
90.3%
ValueCountFrequency (%)
37.43582371 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%
37.43731149 1
0.1%
37.43736832 1
0.1%
37.43737508 1
0.1%
ValueCountFrequency (%)
37.47832209 1
 
0.1%
37.47825228 1
 
0.1%
37.47761813 1
 
0.1%
37.47747322 2
0.1%
37.47745107 1
 
0.1%
37.47740771 1
 
0.1%
37.47728834 1
 
0.1%
37.47698563 3
0.2%
37.47693979 1
 
0.1%
37.47683065 1
 
0.1%

경도
Real number (ℝ)

Distinct1092
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66809
Minimum126.63246
Maximum126.6985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2024-05-03T19:41:25.736108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63246
5-th percentile126.63831
Q1126.6549
median126.67314
Q3126.68148
95-th percentile126.69212
Maximum126.6985
Range0.0660431
Interquartile range (IQR)0.0265879

Descriptive statistics

Standard deviation0.017142555
Coefficient of variation (CV)0.00013533444
Kurtosis-1.0010979
Mean126.66809
Median Absolute Deviation (MAD)0.0117188
Skewness-0.39178116
Sum175688.64
Variance0.00029386718
MonotonicityNot monotonic
2024-05-03T19:41:26.198687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6801618 29
 
2.1%
126.6758414 21
 
1.5%
126.6524737 14
 
1.0%
126.6817327 12
 
0.9%
126.6811632 12
 
0.9%
126.6902724 10
 
0.7%
126.6470946 10
 
0.7%
126.6450629 9
 
0.6%
126.6564 9
 
0.6%
126.6485908 8
 
0.6%
Other values (1082) 1253
90.3%
ValueCountFrequency (%)
126.6324617 1
0.1%
126.6327692 1
0.1%
126.6332704 1
0.1%
126.6337787 1
0.1%
126.633928 1
0.1%
126.634108 1
0.1%
126.6342989 1
0.1%
126.6343038 2
0.1%
126.6343524 1
0.1%
126.6343913 1
0.1%
ValueCountFrequency (%)
126.6985048 3
0.2%
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%
126.6954737 1
 
0.1%
126.6954668 1
 
0.1%
126.6954598 2
0.1%

Interactions

2024-05-03T19:41:13.639845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:11.931973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:12.791613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:13.982099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:12.239877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:13.063649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:14.332255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:12.503282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:13.323972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:41:26.764250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명위도경도
연번1.0000.8860.2240.184
업종명0.8861.0000.1670.139
위도0.2240.1671.0000.703
경도0.1840.1390.7031.000
2024-05-03T19:41:27.073860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도업종명
연번1.0000.107-0.0250.615
위도0.1071.000-0.0140.066
경도-0.025-0.0141.0000.055
업종명0.6150.0660.0551.000

Missing values

2024-05-03T19:41:14.891431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:41:15.291843image/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.
2024-05-03T19:41:15.674264image/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
01미용업훈이미용실인천광역시 미추홀구 남주길125번길 34 (주안동)032-866-132137.455418126.676504
12미용업영미헤어두인천광역시 미추홀구 능해길 38, B동 1층 102호 (용현동, 동아아파트 상가)032-882-142437.458857126.639598
23미용업영헤어뱅크미용실인천광역시 미추홀구 경인로430번길 57 (주안동)032-888-005537.455282126.6877
34미용업새한미용실인천광역시 미추홀구 남주길11번길 19 (주안동)032-866-638037.455167126.670454
45미용업레지나헤어모드미용실인천광역시 미추홀구 석정로 368 (주안동)032-873-468237.467072126.67641
56미용업오미용실인천광역시 미추홀구 제일로40번길 23 (도화동)032-864-637237.457611126.674606
67미용업동그라미미용실인천광역시 미추홀구 미추홀대로614번길 13-34, 다동 1층 102호 (주안동, 미화빌라)032-426-136437.452832126.680973
78미용업선미용실인천광역시 미추홀구 주안서로53번길 69 (도화동)032-862-275637.463443126.674121
89미용업김송희미용실인천광역시 미추홀구 인주대로 179, 1층 (용현동)032-873-812437.455788126.656966
910미용업박혜숙 헤어프로듀서인천광역시 미추홀구 장천로112번길 4 (숭의동)032-885-295337.462781126.656273
연번업종명상호명도로명주소전화번호위도경도
13771378피부미용업, 네일미용업, 화장,분장 미용업네일미기인천광역시 미추홀구 주안로213번길 15, 간석역프라자 1층 104호 (주안동)032-442-824137.464226126.693574
13781379피부미용업, 네일미용업, 화장,분장 미용업네일디디(NAILDD)인천광역시 미추홀구 낙섬서로 6, 1층 (용현동)<NA>37.450247126.633779
13791380피부미용업, 네일미용업, 화장,분장 미용업안녕 속눈썹인천광역시 미추홀구 승학길104번길 29, 2층 (주안동)<NA>37.444353126.68155
13801381피부미용업, 네일미용업, 화장,분장 미용업네일 102(Nail 102)인천광역시 미추홀구 숙골로 120, 1층 103호 (도화동)<NA>37.472606126.661565
13811382피부미용업, 네일미용업, 화장,분장 미용업샤샤네일인천광역시 미추홀구 염전로168번길 28, 도화두손지젤시티 C동 1층 106호 (도화동)<NA>37.476986126.659137
13821383피부미용업, 네일미용업, 화장,분장 미용업쭈네일인천광역시 미추홀구 미추로 29, 지하1층 (숭의동)<NA>37.462686126.646755
13831384피부미용업, 네일미용업, 화장,분장 미용업네일미니인천광역시 미추홀구 한나루로489번길 107, 1층 102호 (용현동)<NA>37.452197126.661419
13841385피부미용업, 네일미용업, 화장,분장 미용업더 예쁘게인천광역시 미추홀구 매소홀로475번길 23, 202호 (학익동)<NA>37.440682126.674818
13851386피부미용업, 네일미용업, 화장,분장 미용업반디인하우스인천광역시 미추홀구 숙골로87번길 5, 204동 2층 2-20, 21호 (도화동, 더샵 인천스카이타워 2단지)<NA>37.469475126.663262
13861387피부미용업, 네일미용업, 화장,분장 미용업모도리 네일(MODORI NAIL)인천광역시 미추홀구 독배로382번길 12, 1층 (용현동)<NA>37.453632126.649205