Overview

Dataset statistics

Number of variables7
Number of observations349
Missing cells967
Missing cells (%)39.6%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory20.2 KiB
Average record size in memory59.4 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description인천광역시 미추홀구 관내에 소재한 숙박업에 대한 데이터로 관내 숙박업에 관한 업종명, 업소명, 전화번호,위도,경도 등을 제공합니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15061015/fileData.do

Alerts

Dataset has 1 (0.3%) duplicate rowsDuplicates
연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
연번 has 161 (46.1%) missing valuesMissing
업소명 has 155 (44.4%) missing valuesMissing
도로명주소 has 155 (44.4%) missing valuesMissing
전화번호 has 174 (49.9%) missing valuesMissing
위도 has 161 (46.1%) missing valuesMissing
경도 has 161 (46.1%) missing valuesMissing

Reproduction

Analysis started2024-04-29 22:37:21.288128
Analysis finished2024-04-29 22:37:24.065839
Duration2.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct188
Distinct (%)100.0%
Missing161
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean94.5
Minimum1
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-30T07:37:24.134825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.35
Q147.75
median94.5
Q3141.25
95-th percentile178.65
Maximum188
Range187
Interquartile range (IQR)93.5

Descriptive statistics

Standard deviation54.415071
Coefficient of variation (CV)0.57582086
Kurtosis-1.2
Mean94.5
Median Absolute Deviation (MAD)47
Skewness0
Sum17766
Variance2961
MonotonicityStrictly increasing
2024-04-30T07:37:24.271986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131 1
 
0.3%
122 1
 
0.3%
123 1
 
0.3%
124 1
 
0.3%
125 1
 
0.3%
126 1
 
0.3%
127 1
 
0.3%
128 1
 
0.3%
129 1
 
0.3%
130 1
 
0.3%
Other values (178) 178
51.0%
(Missing) 161
46.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
188 1
0.3%
187 1
0.3%
186 1
0.3%
185 1
0.3%
184 1
0.3%
183 1
0.3%
182 1
0.3%
181 1
0.3%
180 1
0.3%
179 1
0.3%

업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
숙박업(일반)
175 
<NA>
161 
숙박업(생활)
 
13

Length

Max length7
Median length7
Mean length5.6160458
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 175
50.1%
<NA> 161
46.1%
숙박업(생활) 13
 
3.7%

Length

2024-04-30T07:37:24.410002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:37:24.511180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업(일반 175
50.1%
na 161
46.1%
숙박업(생활 13
 
3.7%

업소명
Text

MISSING 

Distinct180
Distinct (%)92.8%
Missing155
Missing (%)44.4%
Memory size2.9 KiB
2024-04-30T07:37:24.743320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length5.9536082
Min length1

Characters and Unicode

Total characters1155
Distinct characters242
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

Unique168 ?
Unique (%)86.6%

Sample

1st row호수여인숙
2nd row서울여인숙
3rd row용님여인숙
4th row수정여관
5th row수정여인숙
ValueCountFrequency (%)
호텔 14
 
5.8%
테마모텔 4
 
1.7%
모텔 3
 
1.2%
에덴파크여관 2
 
0.8%
hotel 2
 
0.8%
인천 2
 
0.8%
썬플라워 2
 
0.8%
스테이 2
 
0.8%
인하한양레지던스 2
 
0.8%
호텔나무 2
 
0.8%
Other values (197) 207
85.5%
2024-04-30T07:37:25.148604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 98
 
8.5%
모 49
 
4.2%
호 49
 
4.2%
48
 
4.2%
여 48
 
4.2%
스 36
 
3.1%
관 35
 
3.0%
이 32
 
2.8%
장 26
 
2.3%
인 24
 
2.1%
Other values (232) 710
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 915
79.2%
Uppercase Letter 71
 
6.1%
Lowercase Letter 51
 
4.4%
Space Separator 48
 
4.2%
Open Punctuation 21
 
1.8%
Close Punctuation 21
 
1.8%
Decimal Number 14
 
1.2%
Other Punctuation 10
 
0.9%
Dash Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 98
 
10.7%
모 49
 
5.4%
호 49
 
5.4%
여 48
 
5.2%
스 36
 
3.9%
관 35
 
3.8%
이 32
 
3.5%
장 26
 
2.8%
인 24
 
2.6%
리 16
 
1.7%
Other values (183) 502
54.9%
Uppercase Letter
ValueCountFrequency (%)
H 10
14.1%
S 8
11.3%
A 7
 
9.9%
O 6
 
8.5%
E 5
 
7.0%
T 5
 
7.0%
N 3
 
4.2%
D 3
 
4.2%
F 3
 
4.2%
L 3
 
4.2%
Other values (11) 18
25.4%
Lowercase Letter
ValueCountFrequency (%)
t 10
19.6%
e 9
17.6%
l 8
15.7%
a 6
11.8%
o 6
11.8%
r 3
 
5.9%
u 2
 
3.9%
y 2
 
3.9%
w 1
 
2.0%
h 1
 
2.0%
Other values (3) 3
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 2
14.3%
1 2
14.3%
6 2
14.3%
4 2
14.3%
5 2
14.3%
3 2
14.3%
9 1
7.1%
7 1
7.1%
Other Punctuation
ValueCountFrequency (%)
. 6
60.0%
& 2
 
20.0%
, 2
 
20.0%
Space Separator
ValueCountFrequency (%)
48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 915
79.2%
Latin 122
 
10.6%
Common 118
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 98
 
10.7%
모 49
 
5.4%
호 49
 
5.4%
여 48
 
5.2%
스 36
 
3.9%
관 35
 
3.8%
이 32
 
3.5%
장 26
 
2.8%
인 24
 
2.6%
리 16
 
1.7%
Other values (183) 502
54.9%
Latin
ValueCountFrequency (%)
H 10
 
8.2%
t 10
 
8.2%
e 9
 
7.4%
S 8
 
6.6%
l 8
 
6.6%
A 7
 
5.7%
a 6
 
4.9%
O 6
 
4.9%
o 6
 
4.9%
E 5
 
4.1%
Other values (24) 47
38.5%
Common
ValueCountFrequency (%)
48
40.7%
( 21
17.8%
) 21
17.8%
. 6
 
5.1%
- 4
 
3.4%
2 2
 
1.7%
1 2
 
1.7%
6 2
 
1.7%
4 2
 
1.7%
& 2
 
1.7%
Other values (5) 8
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 915
79.2%
ASCII 240
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 98
 
10.7%
모 49
 
5.4%
호 49
 
5.4%
여 48
 
5.2%
스 36
 
3.9%
관 35
 
3.8%
이 32
 
3.5%
장 26
 
2.8%
인 24
 
2.6%
리 16
 
1.7%
Other values (183) 502
54.9%
ASCII
ValueCountFrequency (%)
48
20.0%
( 21
 
8.8%
) 21
 
8.8%
H 10
 
4.2%
t 10
 
4.2%
e 9
 
3.8%
S 8
 
3.3%
l 8
 
3.3%
A 7
 
2.9%
. 6
 
2.5%
Other values (39) 92
38.3%

도로명주소
Text

MISSING 

Distinct186
Distinct (%)95.9%
Missing155
Missing (%)44.4%
Memory size2.9 KiB
2024-04-30T07:37:25.359676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length45
Mean length28.438144
Min length23

Characters and Unicode

Total characters5517
Distinct characters90
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

Unique178 ?
Unique (%)91.8%

Sample

1st row인천광역시 미추홀구 장천로42번길 27 (숭의동)
2nd row인천광역시 미추홀구 한나루로586번길 99 (주안동)
3rd row인천광역시 미추홀구 경인로 338 (주안동)
4th row인천광역시 미추홀구 한나루로 502 (주안동)
5th row인천광역시 미추홀구 주안서로54번길 23 (주안동)
ValueCountFrequency (%)
인천광역시 194
19.3%
미추홀구 194
19.3%
주안동 118
 
11.7%
용현동 41
 
4.1%
숭의동 18
 
1.8%
석바위로 12
 
1.2%
도화동 11
 
1.1%
10 10
 
1.0%
일부호 10
 
1.0%
미추홀대로722번길 9
 
0.9%
Other values (205) 388
38.6%
2024-04-30T07:37:25.695787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
811
 
14.7%
인 240
 
4.4%
미 217
 
3.9%
추 217
 
3.9%
홀 214
 
3.9%
동 204
 
3.7%
천 195
 
3.5%
광 194
 
3.5%
역 194
 
3.5%
시 194
 
3.5%
Other values (80) 2837
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3427
62.1%
Space Separator 811
 
14.7%
Decimal Number 798
 
14.5%
Close Punctuation 194
 
3.5%
Open Punctuation 194
 
3.5%
Dash Punctuation 53
 
1.0%
Other Punctuation 25
 
0.5%
Math Symbol 9
 
0.2%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 240
 
7.0%
미 217
 
6.3%
추 217
 
6.3%
홀 214
 
6.2%
동 204
 
6.0%
천 195
 
5.7%
광 194
 
5.7%
역 194
 
5.7%
시 194
 
5.7%
구 194
 
5.7%
Other values (61) 1364
39.8%
Decimal Number
ValueCountFrequency (%)
1 181
22.7%
3 114
14.3%
4 104
13.0%
2 100
12.5%
5 80
10.0%
7 54
 
6.8%
8 50
 
6.3%
6 47
 
5.9%
9 38
 
4.8%
0 30
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
B 2
33.3%
K 2
33.3%
Space Separator
ValueCountFrequency (%)
811
100.0%
Close Punctuation
ValueCountFrequency (%)
) 194
100.0%
Open Punctuation
ValueCountFrequency (%)
( 194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3427
62.1%
Common 2084
37.8%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 240
 
7.0%
미 217
 
6.3%
추 217
 
6.3%
홀 214
 
6.2%
동 204
 
6.0%
천 195
 
5.7%
광 194
 
5.7%
역 194
 
5.7%
시 194
 
5.7%
구 194
 
5.7%
Other values (61) 1364
39.8%
Common
ValueCountFrequency (%)
811
38.9%
) 194
 
9.3%
( 194
 
9.3%
1 181
 
8.7%
3 114
 
5.5%
4 104
 
5.0%
2 100
 
4.8%
5 80
 
3.8%
7 54
 
2.6%
- 53
 
2.5%
Other values (6) 199
 
9.5%
Latin
ValueCountFrequency (%)
A 2
33.3%
B 2
33.3%
K 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3427
62.1%
ASCII 2090
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
811
38.8%
) 194
 
9.3%
( 194
 
9.3%
1 181
 
8.7%
3 114
 
5.5%
4 104
 
5.0%
2 100
 
4.8%
5 80
 
3.8%
7 54
 
2.6%
- 53
 
2.5%
Other values (9) 205
 
9.8%
Hangul
ValueCountFrequency (%)
인 240
 
7.0%
미 217
 
6.3%
추 217
 
6.3%
홀 214
 
6.2%
동 204
 
6.0%
천 195
 
5.7%
광 194
 
5.7%
역 194
 
5.7%
시 194
 
5.7%
구 194
 
5.7%
Other values (61) 1364
39.8%

전화번호
Text

MISSING 

Distinct172
Distinct (%)98.3%
Missing174
Missing (%)49.9%
Memory size2.9 KiB
2024-04-30T07:37:25.953370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.005714
Min length12

Characters and Unicode

Total characters2101
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

Unique169 ?
Unique (%)96.6%

Sample

1st row032-888-6159
2nd row032-868-2211
3rd row032-865-2653
4th row032-424-7510
5th row032-873-0394
ValueCountFrequency (%)
032-426-9111 2
 
1.1%
032-884-1733 2
 
1.1%
032-873-4143 2
 
1.1%
032-439-8945 1
 
0.6%
032-876-3077 1
 
0.6%
032-888-6159 1
 
0.6%
032-876-6668 1
 
0.6%
032-429-3350 1
 
0.6%
032-437-3041 1
 
0.6%
032-884-1566 1
 
0.6%
Other values (162) 162
92.6%
2024-04-30T07:37:26.305461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 350
16.7%
3 309
14.7%
2 305
14.5%
0 264
12.6%
8 232
11.0%
4 158
7.5%
6 127
 
6.0%
7 102
 
4.9%
1 95
 
4.5%
5 94
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1751
83.3%
Dash Punctuation 350
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 309
17.6%
2 305
17.4%
0 264
15.1%
8 232
13.2%
4 158
9.0%
6 127
7.3%
7 102
 
5.8%
1 95
 
5.4%
5 94
 
5.4%
9 65
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 350
16.7%
3 309
14.7%
2 305
14.5%
0 264
12.6%
8 232
11.0%
4 158
7.5%
6 127
 
6.0%
7 102
 
4.9%
1 95
 
4.5%
5 94
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 350
16.7%
3 309
14.7%
2 305
14.5%
0 264
12.6%
8 232
11.0%
4 158
7.5%
6 127
 
6.0%
7 102
 
4.9%
1 95
 
4.5%
5 94
 
4.5%

위도
Real number (ℝ)

MISSING 

Distinct184
Distinct (%)97.9%
Missing161
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean37.460172
Minimum37.448331
Maximum37.475346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-30T07:37:26.451341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.448331
5-th percentile37.454828
Q137.458387
median37.459629
Q337.462557
95-th percentile37.465709
Maximum37.475346
Range0.02701571
Interquartile range (IQR)0.0041702075

Descriptive statistics

Standard deviation0.0034840242
Coefficient of variation (CV)9.300609 × 10-5
Kurtosis1.7190203
Mean37.460172
Median Absolute Deviation (MAD)0.002029395
Skewness0.18548982
Sum7042.5124
Variance1.2138424 × 10-5
MonotonicityNot monotonic
2024-04-30T07:37:26.589120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.45942582 3
 
0.9%
37.46099828 2
 
0.6%
37.4591191 2
 
0.6%
37.46132082 1
 
0.3%
37.46257752 1
 
0.3%
37.46519772 1
 
0.3%
37.4625508 1
 
0.3%
37.46319972 1
 
0.3%
37.4619747 1
 
0.3%
37.46326044 1
 
0.3%
Other values (174) 174
49.9%
(Missing) 161
46.1%
ValueCountFrequency (%)
37.44833077 1
0.3%
37.45172096 1
0.3%
37.4519643 1
0.3%
37.45205793 1
0.3%
37.45296136 1
0.3%
37.45333751 1
0.3%
37.4542916 1
0.3%
37.45457372 1
0.3%
37.45475481 1
0.3%
37.4547898 1
0.3%
ValueCountFrequency (%)
37.47534648 1
0.3%
37.46779413 1
0.3%
37.46731433 1
0.3%
37.46685498 1
0.3%
37.46622837 1
0.3%
37.46593798 1
0.3%
37.46592298 1
0.3%
37.465878 1
0.3%
37.46575428 1
0.3%
37.46572745 1
0.3%

경도
Real number (ℝ)

MISSING 

Distinct184
Distinct (%)97.9%
Missing161
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean126.66991
Minimum126.63268
Maximum126.69272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-30T07:37:26.750321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63268
5-th percentile126.6351
Q1126.65059
median126.67869
Q3126.68332
95-th percentile126.6884
Maximum126.69272
Range0.0600451
Interquartile range (IQR)0.032726375

Descriptive statistics

Standard deviation0.018660886
Coefficient of variation (CV)0.00014731901
Kurtosis-0.90609917
Mean126.66991
Median Absolute Deviation (MAD)0.00759375
Skewness-0.82093046
Sum23813.943
Variance0.00034822867
MonotonicityNot monotonic
2024-04-30T07:37:26.913199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6379898 3
 
0.9%
126.6795615 2
 
0.6%
126.687283 2
 
0.6%
126.6797538 1
 
0.3%
126.6825805 1
 
0.3%
126.6631428 1
 
0.3%
126.6821269 1
 
0.3%
126.6788993 1
 
0.3%
126.6822624 1
 
0.3%
126.6838097 1
 
0.3%
Other values (174) 174
49.9%
(Missing) 161
46.1%
ValueCountFrequency (%)
126.6326787 1
0.3%
126.6328774 1
0.3%
126.633604 1
0.3%
126.633656 1
0.3%
126.6344994 1
0.3%
126.6345587 1
0.3%
126.6347473 1
0.3%
126.6348303 1
0.3%
126.6349037 1
0.3%
126.6350329 1
0.3%
ValueCountFrequency (%)
126.6927238 1
0.3%
126.6903095 1
0.3%
126.6901583 1
0.3%
126.6895982 1
0.3%
126.6893588 1
0.3%
126.6888287 1
0.3%
126.6888127 1
0.3%
126.6887881 1
0.3%
126.6887817 1
0.3%
126.6884117 1
0.3%

Interactions

2024-04-30T07:37:23.461666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:22.841592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.198931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.549980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.007413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.294011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.640599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.104632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:37:23.379446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:37:27.010653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명위도경도
연번1.0000.8330.2520.465
업종명0.8331.0000.2460.275
위도0.2520.2461.0000.670
경도0.4650.2750.6701.000
2024-04-30T07:37:27.109517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도업종명
연번1.0000.0620.1790.651
위도0.0621.0000.1420.241
경도0.1790.1421.0000.205
업종명0.6510.2410.2051.000

Missing values

2024-04-30T07:37:23.767930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:37:23.874367image/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.
2024-04-30T07:37:23.990379image/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

연번업종명업소명도로명주소전화번호위도경도
01숙박업(일반)호수여인숙인천광역시 미추홀구 장천로42번길 27 (숭의동)032-888-615937.459088126.649548
12숙박업(일반)서울여인숙인천광역시 미추홀구 한나루로586번길 99 (주안동)032-868-221137.457917126.677531
23숙박업(일반)용님여인숙인천광역시 미추홀구 경인로 338 (주안동)<NA>37.458178126.677503
34숙박업(일반)수정여관인천광역시 미추홀구 한나루로 502 (주안동)032-865-265337.451721126.667687
45숙박업(일반)수정여인숙인천광역시 미추홀구 주안서로54번길 23 (주안동)032-424-751037.463726126.679304
56숙박업(일반)주안여관인천광역시 미추홀구 경인로 334-13 (주안동)032-873-039437.458229126.677183
67숙박업(일반)대지모텔인천광역시 미추홀구 미추로 45 (숭의동)032-889-585837.463933126.646112
78숙박업(일반)다모아 모텔인천광역시 미추홀구 수봉로 6-21 (숭의동)032-882-045537.465676126.655571
89숙박업(일반)동성여관인천광역시 미추홀구 미추홀대로722번길 41 (주안동)032-424-304537.461904126.68258
910숙박업(일반)석바위모텔인천광역시 미추홀구 경인로435번길 6 (주안동)032-424-736737.458338126.688788
연번업종명업소명도로명주소전화번호위도경도
339<NA><NA><NA><NA><NA><NA><NA>
340<NA><NA><NA><NA><NA><NA><NA>
341<NA><NA><NA><NA><NA><NA><NA>
342<NA><NA><NA><NA><NA><NA><NA>
343<NA><NA>루시드인천광역시 미추홀구 아암대로 15, 인하한양아이클래스 4~23층 일부호 (용현동)<NA><NA><NA>
344<NA><NA>인하한양레지던스인천광역시 미추홀구 아암대로 15, 인하한양아이클래스 4~24층 일부호 (용현동)<NA><NA><NA>
345<NA><NA>해경스테이 A,B동인천광역시 미추홀구 주안로61번길 8-13, 해경스테이 A, B동 일부호 (주안동)<NA><NA><NA>
346<NA><NA>제이든인천광역시 미추홀구 주안서로54번길 15 (주안동)<NA><NA><NA>
347<NA><NA>인하한양아이클래스 인더시티인천광역시 미추홀구 아암대로 15, 인하한양아이클래스 1~24층 일부호 (용현동)<NA><NA><NA>
348<NA><NA>케이스테이(K-STAY)인천광역시 미추홀구 인주대로4번길 10, 스마트 하우스K 2~16층 일부호 (용현동)<NA><NA><NA>

Duplicate rows

Most frequently occurring

연번업종명업소명도로명주소전화번호위도경도# duplicates
0<NA><NA><NA><NA><NA><NA><NA>155