Overview

Dataset statistics

Number of variables10
Number of observations197
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 KiB
Average record size in memory82.7 B

Variable types

Text4
Categorical4
Numeric2

Alerts

서울 has constant value ""Constant
강남구 has constant value ""Constant
127.06236 is highly overall correlated with 37.508239 and 1 other fieldsHigh correlation
37.508239 is highly overall correlated with 127.06236 and 1 other fieldsHigh correlation
대치동 is highly overall correlated with 127.06236 and 1 other fieldsHigh correlation
2호선 삼성역 4번출구앞 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:40:41.385841
Analysis finished2023-12-10 06:40:43.403195
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2호선 삼성역 4번출구앞
Text

UNIQUE 

Distinct197
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:43.764412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length9.3604061
Min length4

Characters and Unicode

Total characters1844
Distinct characters261
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)100.0%

Sample

1st row2호선 삼성역 중간
2nd row2호선 선능역B1 3번출구
3rd row2호선 역삼역B1 4번출구
4th row3호선 대치역 대합실 양재측
5th row3호선 도곡역 승강장 매봉
ValueCountFrequency (%)
대합실 11
 
2.9%
수인분당선 8
 
2.1%
3호선 8
 
2.1%
1층 7
 
1.9%
승강장 7
 
1.9%
우성아파트 4
 
1.1%
1차 4
 
1.1%
청담 4
 
1.1%
7호선 4
 
1.1%
버스정류장 3
 
0.8%
Other values (286) 316
84.0%
2023-12-10T15:40:44.490427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179
 
9.7%
앞 75
 
4.1%
역 47
 
2.5%
구 42
 
2.3%
1 42
 
2.3%
동 42
 
2.3%
대 40
 
2.2%
선 31
 
1.7%
파 31
 
1.7%
트 29
 
1.6%
Other values (251) 1286
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1492
80.9%
Space Separator 179
 
9.7%
Decimal Number 132
 
7.2%
Uppercase Letter 34
 
1.8%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 75
 
5.0%
역 47
 
3.2%
구 42
 
2.8%
동 42
 
2.8%
대 40
 
2.7%
선 31
 
2.1%
파 31
 
2.1%
트 29
 
1.9%
아 29
 
1.9%
교 26
 
1.7%
Other values (225) 1100
73.7%
Uppercase Letter
ValueCountFrequency (%)
B 7
20.6%
K 4
11.8%
L 4
11.8%
S 3
8.8%
T 3
8.8%
G 3
8.8%
A 2
 
5.9%
H 2
 
5.9%
P 2
 
5.9%
O 2
 
5.9%
Other values (2) 2
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 42
31.8%
2 24
18.2%
3 20
15.2%
4 9
 
6.8%
5 9
 
6.8%
6 7
 
5.3%
0 7
 
5.3%
7 6
 
4.5%
8 5
 
3.8%
9 3
 
2.3%
Space Separator
ValueCountFrequency (%)
179
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1492
80.9%
Common 318
 
17.2%
Latin 34
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 75
 
5.0%
역 47
 
3.2%
구 42
 
2.8%
동 42
 
2.8%
대 40
 
2.7%
선 31
 
2.1%
파 31
 
2.1%
트 29
 
1.9%
아 29
 
1.9%
교 26
 
1.7%
Other values (225) 1100
73.7%
Common
ValueCountFrequency (%)
179
56.3%
1 42
 
13.2%
2 24
 
7.5%
3 20
 
6.3%
4 9
 
2.8%
5 9
 
2.8%
6 7
 
2.2%
0 7
 
2.2%
7 6
 
1.9%
8 5
 
1.6%
Other values (4) 10
 
3.1%
Latin
ValueCountFrequency (%)
B 7
20.6%
K 4
11.8%
L 4
11.8%
S 3
8.8%
T 3
8.8%
G 3
8.8%
A 2
 
5.9%
H 2
 
5.9%
P 2
 
5.9%
O 2
 
5.9%
Other values (2) 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1492
80.9%
ASCII 352
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179
50.9%
1 42
 
11.9%
2 24
 
6.8%
3 20
 
5.7%
4 9
 
2.6%
5 9
 
2.6%
B 7
 
2.0%
6 7
 
2.0%
0 7
 
2.0%
7 6
 
1.7%
Other values (16) 42
 
11.9%
Hangul
ValueCountFrequency (%)
앞 75
 
5.0%
역 47
 
3.2%
구 42
 
2.8%
동 42
 
2.8%
대 40
 
2.7%
선 31
 
2.1%
파 31
 
2.1%
트 29
 
1.9%
아 29
 
1.9%
교 26
 
1.7%
Other values (225) 1100
73.7%

Ext
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Ext
129 
Int
68 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Ext 129
65.5%
Int 68
34.5%

Length

2023-12-10T15:40:44.677951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:44.841058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ext 129
65.5%
int 68
34.5%

서울
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울
197 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 197
100.0%

Length

2023-12-10T15:40:45.042093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:45.208479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 197
100.0%

강남구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
강남구
197 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 197
100.0%

Length

2023-12-10T15:40:45.371655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:45.538189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강남구 197
100.0%

테헤란로
Text

Distinct67
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:45.825398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.822335
Min length1

Characters and Unicode

Total characters753
Distinct characters54
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)23.9%

Sample

1st row테헤란로
2nd row테헤란로
3rd row테헤란로
4th rowX
5th row남부순환로
ValueCountFrequency (%)
x 42
21.3%
테헤란로 15
 
7.6%
학동로 9
 
4.6%
강남대로 9
 
4.6%
압구정로 9
 
4.6%
선릉로 8
 
4.1%
개포로 7
 
3.6%
봉은사로 7
 
3.6%
삼성로 6
 
3.0%
영동대로 6
 
3.0%
Other values (57) 79
40.1%
2023-12-10T15:40:46.406070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 155
20.6%
길 49
 
6.5%
X 42
 
5.6%
1 29
 
3.9%
대 28
 
3.7%
동 19
 
2.5%
남 19
 
2.5%
란 16
 
2.1%
테 16
 
2.1%
헤 16
 
2.1%
Other values (44) 364
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
79.2%
Decimal Number 115
 
15.3%
Uppercase Letter 42
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 155
26.0%
길 49
 
8.2%
대 28
 
4.7%
동 19
 
3.2%
남 19
 
3.2%
란 16
 
2.7%
테 16
 
2.7%
헤 16
 
2.7%
압 13
 
2.2%
구 13
 
2.2%
Other values (33) 252
42.3%
Decimal Number
ValueCountFrequency (%)
1 29
25.2%
3 15
13.0%
5 14
12.2%
2 10
 
8.7%
9 10
 
8.7%
6 9
 
7.8%
4 9
 
7.8%
7 8
 
7.0%
0 6
 
5.2%
8 5
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
X 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
79.2%
Common 115
 
15.3%
Latin 42
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 155
26.0%
길 49
 
8.2%
대 28
 
4.7%
동 19
 
3.2%
남 19
 
3.2%
란 16
 
2.7%
테 16
 
2.7%
헤 16
 
2.7%
압 13
 
2.2%
구 13
 
2.2%
Other values (33) 252
42.3%
Common
ValueCountFrequency (%)
1 29
25.2%
3 15
13.0%
5 14
12.2%
2 10
 
8.7%
9 10
 
8.7%
6 9
 
7.8%
4 9
 
7.8%
7 8
 
7.0%
0 6
 
5.2%
8 5
 
4.3%
Latin
ValueCountFrequency (%)
X 42
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
79.2%
ASCII 157
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 155
26.0%
길 49
 
8.2%
대 28
 
4.7%
동 19
 
3.2%
남 19
 
3.2%
란 16
 
2.7%
테 16
 
2.7%
헤 16
 
2.7%
압 13
 
2.2%
구 13
 
2.2%
Other values (33) 252
42.3%
ASCII
ValueCountFrequency (%)
X 42
26.8%
1 29
18.5%
3 15
 
9.6%
5 14
 
8.9%
2 10
 
6.4%
9 10
 
6.4%
6 9
 
5.7%
4 9
 
5.7%
7 8
 
5.1%
0 6
 
3.8%

534
Text

Distinct130
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:46.877967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.1979695
Min length1

Characters and Unicode

Total characters630
Distinct characters22
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

Unique108 ?
Unique (%)54.8%

Sample

1st row 340 선릉역
2nd row 340
3rd row 156
4th rowX
5th row 2814
ValueCountFrequency (%)
x 42
 
20.9%
20 3
 
1.5%
49 3
 
1.5%
5 3
 
1.5%
57 3
 
1.5%
43 3
 
1.5%
340 3
 
1.5%
508 3
 
1.5%
321 2
 
1.0%
102 2
 
1.0%
Other values (120) 134
66.7%
2023-12-10T15:40:47.555154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159
25.2%
1 77
12.2%
2 75
11.9%
4 49
 
7.8%
3 48
 
7.6%
0 45
 
7.1%
X 42
 
6.7%
5 38
 
6.0%
6 27
 
4.3%
7 20
 
3.2%
Other values (12) 50
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 415
65.9%
Space Separator 159
 
25.2%
Uppercase Letter 42
 
6.7%
Other Letter 12
 
1.9%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 77
18.6%
2 75
18.1%
4 49
11.8%
3 48
11.6%
0 45
10.8%
5 38
9.2%
6 27
 
6.5%
7 20
 
4.8%
9 19
 
4.6%
8 17
 
4.1%
Other Letter
ValueCountFrequency (%)
역 4
33.3%
청 1
 
8.3%
담 1
 
8.3%
언 1
 
8.3%
주 1
 
8.3%
논 1
 
8.3%
현 1
 
8.3%
선 1
 
8.3%
릉 1
 
8.3%
Space Separator
ValueCountFrequency (%)
159
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
91.4%
Latin 42
 
6.7%
Hangul 12
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
159
27.6%
1 77
13.4%
2 75
13.0%
4 49
 
8.5%
3 48
 
8.3%
0 45
 
7.8%
5 38
 
6.6%
6 27
 
4.7%
7 20
 
3.5%
9 19
 
3.3%
Other values (2) 19
 
3.3%
Hangul
ValueCountFrequency (%)
역 4
33.3%
청 1
 
8.3%
담 1
 
8.3%
언 1
 
8.3%
주 1
 
8.3%
논 1
 
8.3%
현 1
 
8.3%
선 1
 
8.3%
릉 1
 
8.3%
Latin
ValueCountFrequency (%)
X 42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 618
98.1%
Hangul 12
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
25.7%
1 77
12.5%
2 75
12.1%
4 49
 
7.9%
3 48
 
7.8%
0 45
 
7.3%
X 42
 
6.8%
5 38
 
6.1%
6 27
 
4.4%
7 20
 
3.2%
Other values (3) 38
 
6.1%
Hangul
ValueCountFrequency (%)
역 4
33.3%
청 1
 
8.3%
담 1
 
8.3%
언 1
 
8.3%
주 1
 
8.3%
논 1
 
8.3%
현 1
 
8.3%
선 1
 
8.3%
릉 1
 
8.3%

대치동
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
대치동
32 
역삼동
27 
논현동
22 
삼성동
18 
청담동
18 
Other values (8)
80 

Length

Max length4
Median length3
Mean length3.0507614
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row삼성동
2nd row삼성동
3rd row역삼동
4th row대치동
5th row도곡동

Common Values

ValueCountFrequency (%)
대치동 32
16.2%
역삼동 27
13.7%
논현동 22
11.2%
삼성동 18
9.1%
청담동 18
9.1%
도곡동 16
8.1%
개포동 16
8.1%
수서동 14
7.1%
일원동 13
6.6%
압구정동 10
 
5.1%
Other values (3) 11
 
5.6%

Length

2023-12-10T15:40:47.791610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대치동 32
16.2%
역삼동 27
13.7%
논현동 22
11.2%
삼성동 18
9.1%
청담동 18
9.1%
도곡동 16
8.1%
개포동 16
8.1%
수서동 14
7.1%
일원동 13
6.6%
압구정동 10
 
5.1%
Other values (3) 11
 
5.6%

946-1
Text

Distinct191
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:48.297132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.3096447
Min length2

Characters and Unicode

Total characters1046
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique185 ?
Unique (%)93.9%

Sample

1st row 172-66
2nd row 172-66
3rd row 804
4th row 600
5th row 339-2
ValueCountFrequency (%)
172-66 2
 
1.0%
66 2
 
1.0%
738 2
 
1.0%
728 2
 
1.0%
77-76 2
 
1.0%
65 2
 
1.0%
458 1
 
0.5%
958-3 1
 
0.5%
546-2 1
 
0.5%
830-23 1
 
0.5%
Other values (181) 181
91.9%
2023-12-10T15:40:49.083091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
197
18.8%
1 132
12.6%
- 117
11.2%
7 87
8.3%
2 79
7.6%
4 67
 
6.4%
0 66
 
6.3%
6 64
 
6.1%
5 64
 
6.1%
3 63
 
6.0%
Other values (3) 110
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
69.9%
Space Separator 197
 
18.8%
Dash Punctuation 117
 
11.2%
Other Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 132
18.1%
7 87
11.9%
2 79
10.8%
4 67
9.2%
0 66
9.0%
6 64
8.8%
5 64
8.8%
3 63
8.6%
9 56
7.7%
8 53
7.3%
Space Separator
ValueCountFrequency (%)
197
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 117
100.0%
Other Letter
ValueCountFrequency (%)
산 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1045
99.9%
Hangul 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
197
18.9%
1 132
12.6%
- 117
11.2%
7 87
8.3%
2 79
7.6%
4 67
 
6.4%
0 66
 
6.3%
6 64
 
6.1%
5 64
 
6.1%
3 63
 
6.0%
Other values (2) 109
10.4%
Hangul
ValueCountFrequency (%)
산 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1045
99.9%
Hangul 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
197
18.9%
1 132
12.6%
- 117
11.2%
7 87
8.3%
2 79
7.6%
4 67
 
6.4%
0 66
 
6.3%
6 64
 
6.1%
5 64
 
6.1%
3 63
 
6.0%
Other values (2) 109
10.4%
Hangul
ValueCountFrequency (%)
산 1
100.0%

127.06236
Real number (ℝ)

HIGH CORRELATION 

Distinct192
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.05333
Minimum127.01938
Maximum127.11021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:49.337579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01938
5-th percentile127.02525
Q1127.03674
median127.04965
Q3127.0617
95-th percentile127.10055
Maximum127.11021
Range0.09083
Interquartile range (IQR)0.02496

Descriptive statistics

Standard deviation0.021826291
Coefficient of variation (CV)0.00017178843
Kurtosis0.094369606
Mean127.05333
Median Absolute Deviation (MAD)0.01256
Skewness0.81625661
Sum25029.506
Variance0.00047638699
MonotonicityNot monotonic
2023-12-10T15:40:49.580705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.058 2
 
1.0%
127.10055 2
 
1.0%
127.04918 2
 
1.0%
127.02946 2
 
1.0%
127.04513 2
 
1.0%
127.1091 1
 
0.5%
127.03946 1
 
0.5%
127.05879 1
 
0.5%
127.0308 1
 
0.5%
127.01938 1
 
0.5%
Other values (182) 182
92.4%
ValueCountFrequency (%)
127.01938 1
0.5%
127.01951 1
0.5%
127.02185 1
0.5%
127.02191 1
0.5%
127.02257 1
0.5%
127.02264 1
0.5%
127.02273 1
0.5%
127.02445 1
0.5%
127.02449 1
0.5%
127.02501 1
0.5%
ValueCountFrequency (%)
127.11021 1
0.5%
127.1091 1
0.5%
127.10754 1
0.5%
127.10709 1
0.5%
127.10531 1
0.5%
127.10512 1
0.5%
127.10343 1
0.5%
127.1026 1
0.5%
127.10135 1
0.5%
127.10055 2
1.0%

37.508239
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.502259
Minimum37.464634
Maximum37.533017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:49.800504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.464634
5-th percentile37.482299
Q137.490829
median37.500459
Q337.514705
95-th percentile37.526343
Maximum37.533017
Range0.068383
Interquartile range (IQR)0.023876

Descriptive statistics

Standard deviation0.015013667
Coefficient of variation (CV)0.00040034034
Kurtosis-0.6808306
Mean37.502259
Median Absolute Deviation (MAD)0.011655
Skewness0.056837845
Sum7387.945
Variance0.0002254102
MonotonicityNot monotonic
2023-12-10T15:40:50.031535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5074 2
 
1.0%
37.518711 2
 
1.0%
37.491116 2
 
1.0%
37.487882 2
 
1.0%
37.498073 2
 
1.0%
37.488248 2
 
1.0%
37.511128 1
 
0.5%
37.529812 1
 
0.5%
37.500459 1
 
0.5%
37.512511 1
 
0.5%
Other values (181) 181
91.9%
ValueCountFrequency (%)
37.464634 1
0.5%
37.464785 1
0.5%
37.465034 1
0.5%
37.471692 1
0.5%
37.47446 1
0.5%
37.475051 1
0.5%
37.477517 1
0.5%
37.478604 1
0.5%
37.480672 1
0.5%
37.481622 1
0.5%
ValueCountFrequency (%)
37.533017 1
0.5%
37.530894 1
0.5%
37.530304 1
0.5%
37.52988 1
0.5%
37.529812 1
0.5%
37.529649 1
0.5%
37.529568 1
0.5%
37.528928 1
0.5%
37.52885 1
0.5%
37.526865 1
0.5%

Interactions

2023-12-10T15:40:42.664733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:42.392007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:42.851055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:42.499789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:40:50.189323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Ext테헤란로대치동127.0623637.508239
Ext1.0000.4810.0000.0790.000
테헤란로0.4811.0000.8980.8410.734
대치동0.0000.8981.0000.8440.885
127.062360.0790.8410.8441.0000.693
37.5082390.0000.7340.8850.6931.000
2023-12-10T15:40:50.353507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대치동Ext
대치동1.0000.000
Ext0.0001.000
2023-12-10T15:40:50.499081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
127.0623637.508239Ext대치동
127.062361.000-0.5460.0570.556
37.508239-0.5461.0000.0000.632
Ext0.0570.0001.0000.000
대치동0.5560.6320.0001.000

Missing values

2023-12-10T15:40:43.080548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:40:43.318115image/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

2호선 삼성역 4번출구앞Ext서울강남구테헤란로534대치동946-1127.0623637.508239
02호선 삼성역 중간Int서울강남구테헤란로340 선릉역삼성동172-66127.05837.5074
12호선 선능역B1 3번출구Int서울강남구테헤란로340삼성동172-66127.05837.5074
22호선 역삼역B1 4번출구Int서울강남구테헤란로156역삼동804127.038337.501239
33호선 대치역 대합실 양재측Int서울강남구XX대치동600127.0591837.492666
43호선 도곡역 승강장 매봉Int서울강남구남부순환로2814도곡동339-2127.0548937.490709
53호선 수서역 대합실 동쪽Int서울강남구광평로270수서동728127.1005537.488248
63호선 수서차량기지 역Int서울강남구밤고개로5길46-13자곡동164127.1102137.481622
73호선 신사역 대합실 1번출구Int서울강남구XX논현동279127.0294637.519818
83호선 압구정역 대합실 4번출구Int서울강남구XX신사동668127.0282837.523533
93호선 일원역 대합실Int서울강남구일원로121일원동717127.0844237.483992
2호선 삼성역 4번출구앞Ext서울강남구테헤란로534대치동946-1127.0623637.508239
187콘티넨탈호텔 2층 마그노리아Int서울강남구테헤란로521삼성동159-8127.0608637.509038
188태우빌딩옆Ext서울강남구학동로337논현동118-16127.039937.517052
189태화기독교복지관 1층Int서울강남구광평로185수서동741127.093337.484838
190풍림빌딩앞Ext서울강남구XX대치동507-4127.0614437.49346
191하나은행앞 삼성Ext서울강남구테헤란로409삼성동141-30127.050337.505341
192학동역 4번출구앞Ext서울강남구논현로665논현동129-8127.0306937.513385
193학동역 5번출구앞Ext서울강남구학동로102 논현역논현동279-67127.0317437.51429
194학빌딩앞 도산공원사Ext서울강남구XX신사동501-5127.029437.520071
195한국타이어Int서울강남구테헤란로133역삼동647-15127.0332737.500047
196한림국제대학원앞Ext서울강남구역삼로405대치동907-13127.0513737.500991