Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Categorical12
Text3
Numeric2

Alerts

CTPRVN_KLANG_NM has constant value ""Constant
SIGNGU_KLANG_NM has constant value ""Constant
CTPRVN_ENGL_NM has constant value ""Constant
SIGNGU_ENGL_NM has constant value ""Constant
CTPRVN_CHNLNG_NM has constant value ""Constant
SIGNGU_CHNLNG_NM has constant value ""Constant
CTPRVN_JLANG_NM has constant value ""Constant
SIGNGU_JLANG_NM has constant value ""Constant
CITY_DO_CD has constant value ""Constant
SIGNGU_CD has constant value ""Constant
REGIST_DE has constant value ""Constant

Reproduction

Analysis started2023-12-10 10:02:08.958435
Analysis finished2023-12-10 10:02:14.597151
Duration5.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SE_NM
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
렌터카
50 
전기차 충전소
27 
LPG 충전소
23 

Length

Max length7
Median length5
Mean length5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLPG 충전소
2nd rowLPG 충전소
3rd row전기차 충전소
4th row렌터카
5th row렌터카

Common Values

ValueCountFrequency (%)
렌터카 50
50.0%
전기차 충전소 27
27.0%
LPG 충전소 23
23.0%

Length

2023-12-10T19:02:14.719491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:14.890313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
렌터카 50
33.3%
충전소 50
33.3%
전기차 27
18.0%
lpg 23
15.3%

FCLTY_NM
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:02:15.225878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length9.46
Min length2

Characters and Unicode

Total characters946
Distinct characters169
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)93.0%

Sample

1st rowGS칼텍스 하슬라충전소
2nd row모전가스충전소
3rd row정동진주차장
4th row독도렌트카강릉지점
5th rowAJ렌터카 강릉역지점
ValueCountFrequency (%)
현대관광렌트카 6
 
4.1%
강릉지점 5
 
3.4%
gs칼텍스 5
 
3.4%
sk행복충전 4
 
2.8%
강릉영업소 3
 
2.1%
강릉역지점 3
 
2.1%
동방엘티에스 3
 
2.1%
롯데렌터카 3
 
2.1%
경포영업소 2
 
1.4%
주민센터 2
 
1.4%
Other values (103) 109
75.2%
2023-12-10T19:02:15.940777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 50
 
5.3%
카 47
 
5.0%
렌 47
 
5.0%
45
 
4.8%
릉 44
 
4.7%
트 39
 
4.1%
소 38
 
4.0%
동 28
 
3.0%
터 27
 
2.9%
전 25
 
2.6%
Other values (159) 556
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 822
86.9%
Uppercase Letter 60
 
6.3%
Space Separator 45
 
4.8%
Decimal Number 7
 
0.7%
Lowercase Letter 4
 
0.4%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 50
 
6.1%
카 47
 
5.7%
렌 47
 
5.7%
릉 44
 
5.4%
트 39
 
4.7%
소 38
 
4.6%
동 28
 
3.4%
터 27
 
3.3%
전 25
 
3.0%
충 22
 
2.7%
Other values (137) 455
55.4%
Uppercase Letter
ValueCountFrequency (%)
S 12
20.0%
K 12
20.0%
G 7
11.7%
T 7
11.7%
X 6
10.0%
L 4
 
6.7%
P 3
 
5.0%
I 2
 
3.3%
J 2
 
3.3%
A 2
 
3.3%
Other values (3) 3
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
50.0%
n 1
25.0%
z 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
1 3
42.9%
Space Separator
ValueCountFrequency (%)
45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 822
86.9%
Latin 64
 
6.8%
Common 60
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 50
 
6.1%
카 47
 
5.7%
렌 47
 
5.7%
릉 44
 
5.4%
트 39
 
4.7%
소 38
 
4.6%
동 28
 
3.4%
터 27
 
3.3%
전 25
 
3.0%
충 22
 
2.7%
Other values (137) 455
55.4%
Latin
ValueCountFrequency (%)
S 12
18.8%
K 12
18.8%
G 7
10.9%
T 7
10.9%
X 6
9.4%
L 4
 
6.2%
P 3
 
4.7%
e 2
 
3.1%
I 2
 
3.1%
J 2
 
3.1%
Other values (6) 7
10.9%
Common
ValueCountFrequency (%)
45
75.0%
2 4
 
6.7%
( 3
 
5.0%
) 3
 
5.0%
1 3
 
5.0%
- 2
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 822
86.9%
ASCII 124
 
13.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 50
 
6.1%
카 47
 
5.7%
렌 47
 
5.7%
릉 44
 
5.4%
트 39
 
4.7%
소 38
 
4.6%
동 28
 
3.4%
터 27
 
3.3%
전 25
 
3.0%
충 22
 
2.7%
Other values (137) 455
55.4%
ASCII
ValueCountFrequency (%)
45
36.3%
S 12
 
9.7%
K 12
 
9.7%
G 7
 
5.6%
T 7
 
5.6%
X 6
 
4.8%
2 4
 
3.2%
L 4
 
3.2%
( 3
 
2.4%
P 3
 
2.4%
Other values (12) 21
16.9%

RN_ADDR
Text

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:02:16.440621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length33
Mean length18.5
Min length10

Characters and Unicode

Total characters1850
Distinct characters116
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

Unique70 ?
Unique (%)70.0%

Sample

1st row강원도 강릉시 강동면 동해대로 1885
2nd row강원도 강릉시 강동면 동해대로 1885
3rd row강원도 강릉시 강동면 정동진역길 52
4th row강원도 강릉시 강릉대로 293
5th row강원도 강릉시 강릉대로 302
ValueCountFrequency (%)
강원도 100
22.7%
강릉시 100
22.7%
하슬라로 18
 
4.1%
경강로 7
 
1.6%
동해대로 7
 
1.6%
율곡로 6
 
1.4%
강릉대로 6
 
1.4%
교동 6
 
1.4%
연곡면 6
 
1.4%
30 5
 
1.1%
Other values (135) 179
40.7%
2023-12-10T19:02:17.384995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
340
18.4%
강 231
 
12.5%
릉 111
 
6.0%
도 102
 
5.5%
시 101
 
5.5%
원 101
 
5.5%
로 77
 
4.2%
1 57
 
3.1%
2 48
 
2.6%
5 39
 
2.1%
Other values (106) 643
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1132
61.2%
Space Separator 340
 
18.4%
Decimal Number 330
 
17.8%
Dash Punctuation 14
 
0.8%
Close Punctuation 12
 
0.6%
Open Punctuation 12
 
0.6%
Uppercase Letter 10
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 231
20.4%
릉 111
 
9.8%
도 102
 
9.0%
시 101
 
8.9%
원 101
 
8.9%
로 77
 
6.8%
동 38
 
3.4%
하 21
 
1.9%
길 21
 
1.9%
번 20
 
1.8%
Other values (88) 309
27.3%
Decimal Number
ValueCountFrequency (%)
1 57
17.3%
2 48
14.5%
5 39
11.8%
3 36
10.9%
4 34
10.3%
6 29
8.8%
0 28
8.5%
8 20
 
6.1%
7 20
 
6.1%
9 19
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
X 3
30.0%
T 3
30.0%
K 3
30.0%
M 1
 
10.0%
Space Separator
ValueCountFrequency (%)
340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1132
61.2%
Common 708
38.3%
Latin 10
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 231
20.4%
릉 111
 
9.8%
도 102
 
9.0%
시 101
 
8.9%
원 101
 
8.9%
로 77
 
6.8%
동 38
 
3.4%
하 21
 
1.9%
길 21
 
1.9%
번 20
 
1.8%
Other values (88) 309
27.3%
Common
ValueCountFrequency (%)
340
48.0%
1 57
 
8.1%
2 48
 
6.8%
5 39
 
5.5%
3 36
 
5.1%
4 34
 
4.8%
6 29
 
4.1%
0 28
 
4.0%
8 20
 
2.8%
7 20
 
2.8%
Other values (4) 57
 
8.1%
Latin
ValueCountFrequency (%)
X 3
30.0%
T 3
30.0%
K 3
30.0%
M 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1132
61.2%
ASCII 718
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
340
47.4%
1 57
 
7.9%
2 48
 
6.7%
5 39
 
5.4%
3 36
 
5.0%
4 34
 
4.7%
6 29
 
4.0%
0 28
 
3.9%
8 20
 
2.8%
7 20
 
2.8%
Other values (8) 67
 
9.3%
Hangul
ValueCountFrequency (%)
강 231
20.4%
릉 111
 
9.8%
도 102
 
9.0%
시 101
 
8.9%
원 101
 
8.9%
로 77
 
6.8%
동 38
 
3.4%
하 21
 
1.9%
길 21
 
1.9%
번 20
 
1.8%
Other values (88) 309
27.3%

CTPRVN_KLANG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원도
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 100
100.0%

Length

2023-12-10T19:02:17.635077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:17.804397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 100
100.0%

SIGNGU_KLANG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강릉시
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강릉시 100
100.0%

Length

2023-12-10T19:02:17.974687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:18.132437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강릉시 100
100.0%

CTPRVN_ENGL_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Gangwon-do
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGangwon-do
2nd rowGangwon-do
3rd rowGangwon-do
4th rowGangwon-do
5th rowGangwon-do

Common Values

ValueCountFrequency (%)
Gangwon-do 100
100.0%

Length

2023-12-10T19:02:18.290495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:18.453071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gangwon-do 100
100.0%

SIGNGU_ENGL_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Gangneung-si
100 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGangneung-si
2nd rowGangneung-si
3rd rowGangneung-si
4th rowGangneung-si
5th rowGangneung-si

Common Values

ValueCountFrequency (%)
Gangneung-si 100
100.0%

Length

2023-12-10T19:02:18.621291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:18.789255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gangneung-si 100
100.0%

CTPRVN_CHNLNG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row江原道
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
江原道 100
100.0%

Length

2023-12-10T19:02:18.995944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:19.153867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 100
100.0%

SIGNGU_CHNLNG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江陵市
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江陵市
2nd row江陵市
3rd row江陵市
4th row江陵市
5th row江陵市

Common Values

ValueCountFrequency (%)
江陵市 100
100.0%

Length

2023-12-10T19:02:19.393180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:19.586974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江陵市 100
100.0%

CTPRVN_JLANG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row江原道
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
江原道 100
100.0%

Length

2023-12-10T19:02:19.855575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:20.075031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 100
100.0%

SIGNGU_JLANG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江陵市（カンヌンシ）
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江陵市（カンヌンシ）
2nd row江陵市（カンヌンシ）
3rd row江陵市（カンヌンシ）
4th row江陵市（カンヌンシ）
5th row江陵市（カンヌンシ）

Common Values

ValueCountFrequency (%)
江陵市（カンヌンシ） 100
100.0%

Length

2023-12-10T19:02:20.315941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:20.497131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江陵市（カンヌンシ） 100
100.0%

CITY_DO_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
32
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32
2nd row32
3rd row32
4th row32
5th row32

Common Values

ValueCountFrequency (%)
32 100
100.0%

Length

2023-12-10T19:02:20.720436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:20.936806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
32 100
100.0%

SIGNGU_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
32030
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32030
2nd row32030
3rd row32030
4th row32030
5th row32030

Common Values

ValueCountFrequency (%)
32030 100
100.0%

Length

2023-12-10T19:02:21.108365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:21.265609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
32030 100
100.0%

LO
Real number (ℝ)

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.89549
Minimum128.80369
Maximum129.05974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:02:21.454520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80369
5-th percentile128.84024
Q1128.87909
median128.8947
Q3128.90813
95-th percentile128.93512
Maximum129.05974
Range0.256049
Interquartile range (IQR)0.0290325

Descriptive statistics

Standard deviation0.03674009
Coefficient of variation (CV)0.00028503782
Kurtosis5.785097
Mean128.89549
Median Absolute Deviation (MAD)0.0154695
Skewness1.2743134
Sum12889.549
Variance0.0013498342
MonotonicityNot monotonic
2023-12-10T19:02:21.710852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.87993 5
 
5.0%
128.87858 4
 
4.0%
128.899857 4
 
4.0%
128.842296 3
 
3.0%
128.900645 2
 
2.0%
128.900096 2
 
2.0%
128.899144 2
 
2.0%
128.889205 2
 
2.0%
128.87627 2
 
2.0%
128.901379 2
 
2.0%
Other values (66) 72
72.0%
ValueCountFrequency (%)
128.803689 1
 
1.0%
128.807599 1
 
1.0%
128.818108 1
 
1.0%
128.837911 2
2.0%
128.840363 1
 
1.0%
128.842296 3
3.0%
128.854202 1
 
1.0%
128.861656 1
 
1.0%
128.874378 1
 
1.0%
128.87547 1
 
1.0%
ValueCountFrequency (%)
129.059738 1
1.0%
129.037918 1
1.0%
128.979258 2
2.0%
128.968581 1
1.0%
128.933354 1
1.0%
128.932198 1
1.0%
128.929889 2
2.0%
128.927597 1
1.0%
128.926659 1
1.0%
128.924697 1
1.0%

LA
Real number (ℝ)

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.765677
Minimum37.612671
Maximum37.910863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:02:21.968247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.612671
5-th percentile37.737884
Q137.7552
median37.76276
Q337.76997
95-th percentile37.845937
Maximum37.910863
Range0.298192
Interquartile range (IQR)0.0147705

Descriptive statistics

Standard deviation0.037834855
Coefficient of variation (CV)0.0010018318
Kurtosis7.2511626
Mean37.765677
Median Absolute Deviation (MAD)0.00756
Skewness-0.043854564
Sum3776.5677
Variance0.0014314762
MonotonicityNot monotonic
2023-12-10T19:02:22.374807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.7552 5
 
5.0%
37.755219 4
 
4.0%
37.76451 4
 
4.0%
37.845937 3
 
3.0%
37.76209 2
 
2.0%
37.763617 2
 
2.0%
37.76375 2
 
2.0%
37.774493 2
 
2.0%
37.783537 2
 
2.0%
37.76276 2
 
2.0%
Other values (66) 72
72.0%
ValueCountFrequency (%)
37.612671 1
1.0%
37.616208 1
1.0%
37.679234 1
1.0%
37.717007 2
2.0%
37.738983 1
1.0%
37.741605 1
1.0%
37.744193 1
1.0%
37.744904 1
1.0%
37.746173 1
1.0%
37.747166 1
1.0%
ValueCountFrequency (%)
37.910863 1
 
1.0%
37.870133 2
2.0%
37.845937 3
3.0%
37.845834 1
 
1.0%
37.816289 1
 
1.0%
37.802595 1
 
1.0%
37.797034 1
 
1.0%
37.79057 1
 
1.0%
37.789988 1
 
1.0%
37.783537 2
2.0%

TEL_NO
Text

Distinct98
Distinct (%)99.0%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:02:22.831394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.060606
Min length9

Characters and Unicode

Total characters1194
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

Unique97 ?
Unique (%)98.0%

Sample

1st row033-643-7766
2nd row033-632-8000
3rd row010-9480-3235
4th row033-243-8000
5th row033-764-8000
ValueCountFrequency (%)
033-764-8000 2
 
2.0%
033-806-7777 1
 
1.0%
033-435-9122 1
 
1.0%
033-333-4001 1
 
1.0%
033-761-7119 1
 
1.0%
033-455-1514 1
 
1.0%
033-641-7901 1
 
1.0%
033-744-7778 1
 
1.0%
033-645-3164 1
 
1.0%
033-735-2121 1
 
1.0%
Other values (88) 88
88.9%
2023-12-10T19:02:23.868046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 242
20.3%
- 196
16.4%
0 193
16.2%
7 88
 
7.4%
4 84
 
7.0%
6 83
 
7.0%
5 82
 
6.9%
1 80
 
6.7%
2 56
 
4.7%
8 52
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 998
83.6%
Dash Punctuation 196
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 242
24.2%
0 193
19.3%
7 88
 
8.8%
4 84
 
8.4%
6 83
 
8.3%
5 82
 
8.2%
1 80
 
8.0%
2 56
 
5.6%
8 52
 
5.2%
9 38
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 242
20.3%
- 196
16.4%
0 193
16.2%
7 88
 
7.4%
4 84
 
7.0%
6 83
 
7.0%
5 82
 
6.9%
1 80
 
6.7%
2 56
 
4.7%
8 52
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 242
20.3%
- 196
16.4%
0 193
16.2%
7 88
 
7.4%
4 84
 
7.0%
6 83
 
7.0%
5 82
 
6.9%
1 80
 
6.7%
2 56
 
4.7%
8 52
 
4.4%

REGIST_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-09-11
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-11
2nd row2020-09-11
3rd row2020-09-11
4th row2020-09-11
5th row2020-09-11

Common Values

ValueCountFrequency (%)
2020-09-11 100
100.0%

Length

2023-12-10T19:02:24.104099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:24.265513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-11 100
100.0%

Interactions

2023-12-10T19:02:13.882580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:13.562159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:13.993250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:13.749961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:02:24.369039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SE_NMFCLTY_NMRN_ADDRLOLATEL_NO
SE_NM1.0001.0001.0000.5810.6360.783
FCLTY_NM1.0001.0000.9990.9730.9570.997
RN_ADDR1.0000.9991.0001.0001.0000.994
LO0.5810.9731.0001.0000.8861.000
LA0.6360.9571.0000.8861.0001.000
TEL_NO0.7830.9970.9941.0001.0001.000
2023-12-10T19:02:24.542232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOLASE_NM
LO1.000-0.0690.441
LA-0.0691.0000.342
SE_NM0.4410.3421.000

Missing values

2023-12-10T19:02:14.170070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:02:14.479410image/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

SE_NMFCLTY_NMRN_ADDRCTPRVN_KLANG_NMSIGNGU_KLANG_NMCTPRVN_ENGL_NMSIGNGU_ENGL_NMCTPRVN_CHNLNG_NMSIGNGU_CHNLNG_NMCTPRVN_JLANG_NMSIGNGU_JLANG_NMCITY_DO_CDSIGNGU_CDLOLATEL_NOREGIST_DE
0LPG 충전소GS칼텍스 하슬라충전소강원도 강릉시 강동면 동해대로 1885강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.97925837.717007<NA>2020-09-11
1LPG 충전소모전가스충전소강원도 강릉시 강동면 동해대로 1885강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.97925837.717007033-643-77662020-09-11
2전기차 충전소정동진주차장강원도 강릉시 강동면 정동진역길 52강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.96858137.679234033-632-80002020-09-11
3렌터카독도렌트카강릉지점강원도 강릉시 강릉대로 293강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.90064337.763811010-9480-32352020-09-11
4렌터카AJ렌터카 강릉역지점강원도 강릉시 강릉대로 302강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.90151237.763444033-243-80002020-09-11
5전기차 충전소강릉시청강원도 강릉시 강릉대로 33(홍제동)강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.8754737.753251033-764-80002020-09-11
6렌터카장기렌트강원도 강릉시 강릉대로 555-5 1층강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.91861137.781368033-655-00532020-09-11
7전기차 충전소한국전력강릉지사강원도 강릉시 강릉대로 563(포남동)강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.91895937.782023033-253-44452020-09-11
8렌터카AJ렌터카 강릉터미널지점강원도 강릉시 강릉대로 90강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.88132437.754073033-734-56112020-09-11
9LPG 충전소S-OIL 동아가스충전소강원도 강릉시 강변로 548강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.92010437.766264033-748-00152020-09-11
SE_NMFCLTY_NMRN_ADDRCTPRVN_KLANG_NMSIGNGU_KLANG_NMCTPRVN_ENGL_NMSIGNGU_ENGL_NMCTPRVN_CHNLNG_NMSIGNGU_CHNLNG_NMCTPRVN_JLANG_NMSIGNGU_JLANG_NMCITY_DO_CDSIGNGU_CDLOLATEL_NOREGIST_DE
90렌터카강원렌트카강릉영업소강원도 강릉시 하슬라로 30강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.8785837.755219033-458-66652020-09-11
91렌터카대한렌트카 강릉영업소강원도 강릉시 하슬라로 30-2강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.87835437.755347033-764-80002020-09-11
92렌터카붕붕렌터카강원도 강릉시 하슬라로 38강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.87764937.755244033-761-87772020-09-11
93렌터카코바렌터카(주)강원도 강릉시 하슬라로 40강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.8773137.75531033-645-31822020-09-11
94전기차 충전소포남2동주민센터강원도 강릉시 하평2길 29강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.91333937.77275033-535-58582020-09-11
95전기차 충전소청송아파트강원도 강릉시 하평길 41(포남동 청송아파트)강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.91345637.771109033-681-51122020-09-11
96렌터카유성렌트카강원도 강릉시 해안로 127강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.93219837.780342033-645-71182020-09-11
97전기차 충전소옥계면사무소강원도 강릉시 현내시장길 30강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030129.03791837.612671010-8773-44462020-09-11
98전기차 충전소홍제동공영주차장강원도 강릉시 홍제동 156번지강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.8826437.753099033-763-77112020-09-11
99렌터카터미널렌트가강원도 강릉시 홍제동 233-8강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.88031637.755206033-761-54002020-09-11