Overview

Dataset statistics

Number of variables27
Number of observations100
Missing cells253
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory228.3 B

Variable types

Text10
Categorical8
Numeric8
Unsupported1

Alerts

lclas has constant value ""Constant
mlsfc has constant value ""Constant
lst_updt_dt has constant value ""Constant
data_orgn has constant value ""Constant
file_name has constant value ""Constant
base_ymd has constant value ""Constant
branch_nm has 63 (63.0%) missing valuesMissing
sub_nm has 100 (100.0%) missing valuesMissing
ri_nm has 84 (84.0%) missing valuesMissing
rd_cd has 2 (2.0%) missing valuesMissing
rd_nm has 2 (2.0%) missing valuesMissing
bld_num has 2 (2.0%) missing valuesMissing
id has unique valuesUnique
id_poi has unique valuesUnique
pnu has unique valuesUnique
beonji has unique valuesUnique
x has unique valuesUnique
y has unique valuesUnique
grid_cd has unique valuesUnique
sub_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:14:23.983406
Analysis finished2023-12-10 10:14:25.134331
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:14:25.380699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowKCDMTPO21N000000001
2nd rowKCDMTPO21N000001287
3rd rowKCDMTPO21N000000003
4th rowKCDMTPO21N000000004
5th rowKCDMTPO21N000000005
ValueCountFrequency (%)
kcdmtpo21n000000001 1
 
1.0%
kcdmtpo21n000000063 1
 
1.0%
kcdmtpo21n000000074 1
 
1.0%
kcdmtpo21n000000073 1
 
1.0%
kcdmtpo21n000000072 1
 
1.0%
kcdmtpo21n000000071 1
 
1.0%
kcdmtpo21n000000070 1
 
1.0%
kcdmtpo21n000000069 1
 
1.0%
kcdmtpo21n000000068 1
 
1.0%
kcdmtpo21n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:14:26.245232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 711
37.4%
1 124
 
6.5%
2 121
 
6.4%
K 100
 
5.3%
O 100
 
5.3%
C 100
 
5.3%
N 100
 
5.3%
P 100
 
5.3%
T 100
 
5.3%
M 100
 
5.3%
Other values (8) 244
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
57.9%
Uppercase Letter 800
42.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 711
64.6%
1 124
 
11.3%
2 121
 
11.0%
8 23
 
2.1%
7 21
 
1.9%
9 21
 
1.9%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
3 19
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
K 100
12.5%
O 100
12.5%
C 100
12.5%
N 100
12.5%
P 100
12.5%
T 100
12.5%
M 100
12.5%
D 100
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
57.9%
Latin 800
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 711
64.6%
1 124
 
11.3%
2 121
 
11.0%
8 23
 
2.1%
7 21
 
1.9%
9 21
 
1.9%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
3 19
 
1.7%
Latin
ValueCountFrequency (%)
K 100
12.5%
O 100
12.5%
C 100
12.5%
N 100
12.5%
P 100
12.5%
T 100
12.5%
M 100
12.5%
D 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 711
37.4%
1 124
 
6.5%
2 121
 
6.4%
K 100
 
5.3%
O 100
 
5.3%
C 100
 
5.3%
N 100
 
5.3%
P 100
 
5.3%
T 100
 
5.3%
M 100
 
5.3%
Other values (8) 244
 
12.8%

lclas
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장소 100
100.0%

Length

2023-12-10T19:14:26.680146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:26.979617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장소 100
100.0%

mlsfc
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문화시설 100
100.0%

Length

2023-12-10T19:14:27.314423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:27.522580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화시설 100
100.0%

id_poi
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16769575
Minimum7232971
Maximum22204983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:27.738000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7232971
5-th percentile7233085.7
Q114010037
median18265905
Q321686617
95-th percentile21833646
Maximum22204983
Range14972012
Interquartile range (IQR)7676579.8

Descriptive statistics

Standard deviation5238099.4
Coefficient of variation (CV)0.31235731
Kurtosis-0.70887433
Mean16769575
Median Absolute Deviation (MAD)3420760.5
Skewness-0.83072322
Sum1.6769575 × 109
Variance2.7437685 × 1013
MonotonicityNot monotonic
2023-12-10T19:14:28.422883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7232990 1
 
1.0%
7233060 1
 
1.0%
18268264 1
 
1.0%
17954717 1
 
1.0%
17308609 1
 
1.0%
17247589 1
 
1.0%
17243112 1
 
1.0%
16891287 1
 
1.0%
16511695 1
 
1.0%
10253716 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
7232971 1
1.0%
7232990 1
1.0%
7233037 1
1.0%
7233044 1
1.0%
7233060 1
1.0%
7233087 1
1.0%
7233124 1
1.0%
7233130 1
1.0%
7233167 1
1.0%
7233196 1
1.0%
ValueCountFrequency (%)
22204983 1
1.0%
22162476 1
1.0%
22084643 1
1.0%
21872420 1
1.0%
21866434 1
1.0%
21831920 1
1.0%
21823755 1
1.0%
21821181 1
1.0%
21688285 1
1.0%
21688282 1
1.0%

poi_nm
Text

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:14:28.915663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.09
Min length3

Characters and Unicode

Total characters609
Distinct characters160
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

Unique65 ?
Unique (%)65.0%

Sample

1st row메가박스
2nd rowEDVD비디오방
3rd row롯데시네마
4th row아이맥스영화관
5th row명성극장
ValueCountFrequency (%)
메가박스 13
 
13.0%
롯데시네마 10
 
10.0%
cgv 10
 
10.0%
카멜레온dvd영화관 2
 
2.0%
동부dvd존dvd영화관 1
 
1.0%
아트씨어터씨앤씨 1
 
1.0%
씨씨방 1
 
1.0%
imaxdvd 1
 
1.0%
무비존 1
 
1.0%
너와나dvd영화감상실 1
 
1.0%
Other values (59) 59
59.0%
2023-12-10T19:14:29.753304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 40
 
6.6%
V 31
 
5.1%
24
 
3.9%
24
 
3.9%
22
 
3.6%
22
 
3.6%
18
 
3.0%
16
 
2.6%
15
 
2.5%
14
 
2.3%
Other values (150) 383
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 501
82.3%
Uppercase Letter 101
 
16.6%
Decimal Number 7
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24
 
4.8%
24
 
4.8%
22
 
4.4%
22
 
4.4%
18
 
3.6%
16
 
3.2%
15
 
3.0%
14
 
2.8%
14
 
2.8%
13
 
2.6%
Other values (132) 319
63.7%
Uppercase Letter
ValueCountFrequency (%)
D 40
39.6%
V 31
30.7%
G 10
 
9.9%
C 10
 
9.9%
E 2
 
2.0%
A 2
 
2.0%
M 1
 
1.0%
X 1
 
1.0%
I 1
 
1.0%
Q 1
 
1.0%
Other values (2) 2
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 2
28.6%
2 1
14.3%
0 1
14.3%
6 1
14.3%
3 1
14.3%
5 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 501
82.3%
Latin 101
 
16.6%
Common 7
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24
 
4.8%
24
 
4.8%
22
 
4.4%
22
 
4.4%
18
 
3.6%
16
 
3.2%
15
 
3.0%
14
 
2.8%
14
 
2.8%
13
 
2.6%
Other values (132) 319
63.7%
Latin
ValueCountFrequency (%)
D 40
39.6%
V 31
30.7%
G 10
 
9.9%
C 10
 
9.9%
E 2
 
2.0%
A 2
 
2.0%
M 1
 
1.0%
X 1
 
1.0%
I 1
 
1.0%
Q 1
 
1.0%
Other values (2) 2
 
2.0%
Common
ValueCountFrequency (%)
1 2
28.6%
2 1
14.3%
0 1
14.3%
6 1
14.3%
3 1
14.3%
5 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 501
82.3%
ASCII 108
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 40
37.0%
V 31
28.7%
G 10
 
9.3%
C 10
 
9.3%
E 2
 
1.9%
A 2
 
1.9%
1 2
 
1.9%
M 1
 
0.9%
X 1
 
0.9%
I 1
 
0.9%
Other values (8) 8
 
7.4%
Hangul
ValueCountFrequency (%)
24
 
4.8%
24
 
4.8%
22
 
4.4%
22
 
4.4%
18
 
3.6%
16
 
3.2%
15
 
3.0%
14
 
2.8%
14
 
2.8%
13
 
2.6%
Other values (132) 319
63.7%

branch_nm
Text

MISSING 

Distinct37
Distinct (%)100.0%
Missing63
Missing (%)63.0%
Memory size932.0 B
2023-12-10T19:14:30.119299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.8378378
Min length3

Characters and Unicode

Total characters142
Distinct characters65
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row대전점
2nd row서귀포점
3rd row원주점
4th row논산점
5th row동백점
ValueCountFrequency (%)
거제점 1
 
2.7%
전주혁신점 1
 
2.7%
서울대입구점 1
 
2.7%
북대구점 1
 
2.7%
경산점 1
 
2.7%
파주문산점 1
 
2.7%
구리아울렛점 1
 
2.7%
경산하양점 1
 
2.7%
수유점 1
 
2.7%
강동점 1
 
2.7%
Other values (27) 27
73.0%
2023-12-10T19:14:30.729149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
26.1%
8
 
5.6%
6
 
4.2%
5
 
3.5%
4
 
2.8%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
2
 
1.4%
Other values (55) 67
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
99.3%
Decimal Number 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
37
26.2%
8
 
5.7%
6
 
4.3%
5
 
3.5%
4
 
2.8%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
2
 
1.4%
Other values (54) 66
46.8%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
99.3%
Common 1
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
37
26.2%
8
 
5.7%
6
 
4.3%
5
 
3.5%
4
 
2.8%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
2
 
1.4%
Other values (54) 66
46.8%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
99.3%
ASCII 1
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
37
26.2%
8
 
5.7%
6
 
4.3%
5
 
3.5%
4
 
2.8%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
2
 
1.4%
Other values (54) 66
46.8%
ASCII
ValueCountFrequency (%)
2 1
100.0%

sub_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

mcate_cd
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97417.92
Minimum92005
Maximum100212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:30.963472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92005
5-th percentile92005
Q192005
median100202
Q3100208
95-th percentile100212
Maximum100212
Range8207
Interquartile range (IQR)8203

Descriptive statistics

Standard deviation3904.6458
Coefficient of variation (CV)0.040081392
Kurtosis-1.5612429
Mean97417.92
Median Absolute Deviation (MAD)7
Skewness-0.68584928
Sum9741792
Variance15246259
MonotonicityNot monotonic
2023-12-10T19:14:31.157373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
92005 34
34.0%
100202 27
27.0%
100212 13
 
13.0%
100209 10
 
10.0%
100208 10
 
10.0%
100207 6
 
6.0%
ValueCountFrequency (%)
92005 34
34.0%
100202 27
27.0%
100207 6
 
6.0%
100208 10
 
10.0%
100209 10
 
10.0%
100212 13
 
13.0%
ValueCountFrequency (%)
100212 13
 
13.0%
100209 10
 
10.0%
100208 10
 
10.0%
100207 6
 
6.0%
100202 27
27.0%
92005 34
34.0%

mcate_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
비디오/DVD감상실
34 
일반극장/영화관
27 
메가박스
13 
롯데시네마
10 
CGV
10 

Length

Max length10
Median length8
Mean length7.3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row메가박스
2nd row비디오/DVD감상실
3rd row롯데시네마
4th row일반극장/영화관
5th row일반극장/영화관

Common Values

ValueCountFrequency (%)
비디오/DVD감상실 34
34.0%
일반극장/영화관 27
27.0%
메가박스 13
 
13.0%
롯데시네마 10
 
10.0%
CGV 10
 
10.0%
자동차전용극장 6
 
6.0%

Length

2023-12-10T19:14:31.414214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:31.616655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비디오/dvd감상실 34
34.0%
일반극장/영화관 27
27.0%
메가박스 13
 
13.0%
롯데시네마 10
 
10.0%
cgv 10
 
10.0%
자동차전용극장 6
 
6.0%

pnu
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5471839 × 1018
Minimum1.1110137 × 1018
Maximum5.0130102 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:31.849546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110137 × 1018
5-th percentile1.1260102 × 1018
Q12.7890132 × 1018
median4.1335177 × 1018
Q34.4215104 × 1018
95-th percentile4.8253107 × 1018
Maximum5.0130102 × 1018
Range3.9019965 × 1018
Interquartile range (IQR)1.6324972 × 1018

Descriptive statistics

Standard deviation1.2191875 × 1018
Coefficient of variation (CV)0.34370576
Kurtosis-0.35440035
Mean3.5471839 × 1018
Median Absolute Deviation (MAD)5.5014955 × 1017
Skewness-0.97763614
Sum4.2302574 × 1018
Variance1.4864183 × 1036
MonotonicityNot monotonic
2023-12-10T19:14:32.104876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3017010600107460000 1
 
1.0%
2723012400108950002 1
 
1.0%
1130510300101680012 1
 
1.0%
4729025021106070000 1
 
1.0%
4831010900109610081 1
 
1.0%
4719010100101290020 1
 
1.0%
4131010300104190007 1
 
1.0%
4148025022108920000 1
 
1.0%
4729011200104390007 1
 
1.0%
4480025021103240001 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1111013700102880000 1
1.0%
1114011700100030003 1
1.0%
1114015900100180005 1
1.0%
1121510700100070011 1
1.0%
1126010200101300003 1
1.0%
1126010200101300130 1
1.0%
1129010300105380098 1
1.0%
1130510300101680012 1
1.0%
1135010500105930003 1
1.0%
1144012000104900000 1
1.0%
ValueCountFrequency (%)
5013010200109140000 1
1.0%
4884025024107600000 1
1.0%
4831032023100950001 1
1.0%
4831010900109610081 1
1.0%
4831010800112110000 1
1.0%
4825010700114870006 1
1.0%
4729025021106070000 1
1.0%
4729011200104390007 1
1.0%
4725010700100010004 1
1.0%
4723011300109820034 1
1.0%

sido_nm
Categorical

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
24 
서울특별시
15 
경상북도
부산광역시
강원도
Other values (11)
39 

Length

Max length7
Median length5
Mean length4.15
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row대전광역시
2nd row경기도
3rd row제주특별자치도
4th row강원도
5th row경상북도

Common Values

ValueCountFrequency (%)
경기도 24
24.0%
서울특별시 15
15.0%
경상북도 8
 
8.0%
부산광역시 8
 
8.0%
강원도 6
 
6.0%
전라북도 6
 
6.0%
경상남도 5
 
5.0%
충청남도 5
 
5.0%
충청북도 5
 
5.0%
대전광역시 4
 
4.0%
Other values (6) 14
14.0%

Length

2023-12-10T19:14:32.358663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 24
24.0%
서울특별시 15
15.0%
경상북도 8
 
8.0%
부산광역시 8
 
8.0%
강원도 6
 
6.0%
전라북도 6
 
6.0%
경상남도 5
 
5.0%
충청남도 5
 
5.0%
충청북도 5
 
5.0%
대전광역시 4
 
4.0%
Other values (6) 14
14.0%

sgg_nm
Text

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:14:32.782228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.7
Min length2

Characters and Unicode

Total characters370
Distinct characters86
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)56.0%

Sample

1st row서구
2nd row부천시
3rd row서귀포시
4th row속초시
5th row상주시
ValueCountFrequency (%)
중구 7
 
6.0%
전주시 4
 
3.4%
거제시 3
 
2.6%
완산구 3
 
2.6%
부산진구 3
 
2.6%
안산시 3
 
2.6%
북구 3
 
2.6%
충주시 2
 
1.7%
용인시 2
 
1.7%
청주시 2
 
1.7%
Other values (71) 85
72.6%
2023-12-10T19:14:33.476265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
 
15.7%
56
 
15.1%
18
 
4.9%
17
 
4.6%
17
 
4.6%
9
 
2.4%
7
 
1.9%
7
 
1.9%
7
 
1.9%
6
 
1.6%
Other values (76) 168
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 353
95.4%
Space Separator 17
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
58
 
16.4%
56
 
15.9%
18
 
5.1%
17
 
4.8%
9
 
2.5%
7
 
2.0%
7
 
2.0%
7
 
2.0%
6
 
1.7%
6
 
1.7%
Other values (75) 162
45.9%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 353
95.4%
Common 17
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
58
 
16.4%
56
 
15.9%
18
 
5.1%
17
 
4.8%
9
 
2.5%
7
 
2.0%
7
 
2.0%
7
 
2.0%
6
 
1.7%
6
 
1.7%
Other values (75) 162
45.9%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 353
95.4%
ASCII 17
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
58
 
16.4%
56
 
15.9%
18
 
5.1%
17
 
4.8%
9
 
2.5%
7
 
2.0%
7
 
2.0%
7
 
2.0%
6
 
1.7%
6
 
1.7%
Other values (75) 162
45.9%
ASCII
ValueCountFrequency (%)
17
100.0%
Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:14:33.998222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.04
Min length2

Characters and Unicode

Total characters304
Distinct characters95
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)86.0%

Sample

1st row탄방동
2nd row상동
3rd row법환동
4th row조양동
5th row남성동
ValueCountFrequency (%)
부전동 2
 
2.0%
원동 2
 
2.0%
고잔동 2
 
2.0%
상봉동 2
 
2.0%
고사동 2
 
2.0%
조양동 2
 
2.0%
인창동 2
 
2.0%
수유동 1
 
1.0%
탄방동 1
 
1.0%
원평동 1
 
1.0%
Other values (83) 83
83.0%
2023-12-10T19:14:34.866826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
28.6%
9
 
3.0%
7
 
2.3%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.6%
5
 
1.6%
5
 
1.6%
Other values (85) 160
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
98.4%
Decimal Number 5
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
87
29.1%
9
 
3.0%
7
 
2.3%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (83) 155
51.8%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
4 2
40.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
98.4%
Common 5
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
87
29.1%
9
 
3.0%
7
 
2.3%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (83) 155
51.8%
Common
ValueCountFrequency (%)
1 3
60.0%
4 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
98.4%
ASCII 5
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
87
29.1%
9
 
3.0%
7
 
2.3%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (83) 155
51.8%
ASCII
ValueCountFrequency (%)
1 3
60.0%
4 2
40.0%

ri_nm
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing84
Missing (%)84.0%
Memory size932.0 B
2023-12-10T19:14:35.182800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.875
Min length2

Characters and Unicode

Total characters46
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row구천리
2nd row팔봉리
3rd row삼정리
4th row수입리
5th row교리
ValueCountFrequency (%)
구천리 1
 
6.2%
팔봉리 1
 
6.2%
삼정리 1
 
6.2%
수입리 1
 
6.2%
교리 1
 
6.2%
남계리 1
 
6.2%
월문리 1
 
6.2%
오관리 1
 
6.2%
당동리 1
 
6.2%
금락리 1
 
6.2%
Other values (6) 6
37.5%
2023-12-10T19:14:35.720938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
34.8%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (21) 21
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
16
34.8%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (21) 21
45.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
16
34.8%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (21) 21
45.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
16
34.8%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (21) 21
45.7%

beonji
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:14:36.252448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.62
Min length2

Characters and Unicode

Total characters462
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

Unique100 ?
Unique (%)100.0%

Sample

1st row746
2nd row456
3rd row914
4th row1545-1
5th row1-4
ValueCountFrequency (%)
746 1
 
1.0%
471 1
 
1.0%
607 1
 
1.0%
961-81 1
 
1.0%
129-20 1
 
1.0%
419-7 1
 
1.0%
892 1
 
1.0%
439-7 1
 
1.0%
324-1 1
 
1.0%
109-3 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:14:37.175872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 85
18.4%
- 73
15.8%
3 45
9.7%
2 42
9.1%
8 37
8.0%
9 36
7.8%
4 32
 
6.9%
5 31
 
6.7%
6 30
 
6.5%
0 29
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 389
84.2%
Dash Punctuation 73
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 85
21.9%
3 45
11.6%
2 42
10.8%
8 37
9.5%
9 36
9.3%
4 32
 
8.2%
5 31
 
8.0%
6 30
 
7.7%
0 29
 
7.5%
7 22
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 85
18.4%
- 73
15.8%
3 45
9.7%
2 42
9.1%
8 37
8.0%
9 36
7.8%
4 32
 
6.9%
5 31
 
6.7%
6 30
 
6.5%
0 29
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 85
18.4%
- 73
15.8%
3 45
9.7%
2 42
9.1%
8 37
8.0%
9 36
7.8%
4 32
 
6.9%
5 31
 
6.7%
6 30
 
6.5%
0 29
 
6.3%

badm_cd
Real number (ℝ)

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5471839 × 109
Minimum1.1110137 × 109
Maximum5.0130102 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:37.433837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110137 × 109
5-th percentile1.1260102 × 109
Q12.7890132 × 109
median4.1335177 × 109
Q34.4215104 × 109
95-th percentile4.8253107 × 109
Maximum5.0130102 × 109
Range3.9019965 × 109
Interquartile range (IQR)1.6324972 × 109

Descriptive statistics

Standard deviation1.2191875 × 109
Coefficient of variation (CV)0.34370576
Kurtosis-0.35440035
Mean3.5471839 × 109
Median Absolute Deviation (MAD)5.5014955 × 108
Skewness-0.97763614
Sum3.5471839 × 1011
Variance1.4864183 × 1018
MonotonicityNot monotonic
2023-12-10T19:14:37.713103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2623010300 2
 
2.0%
4221010800 2
 
2.0%
4131010300 2
 
2.0%
4137010300 2
 
2.0%
1126010200 2
 
2.0%
4127310100 2
 
2.0%
4511111700 2
 
2.0%
3017010600 1
 
1.0%
4831010900 1
 
1.0%
4719010100 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
1111013700 1
1.0%
1114011700 1
1.0%
1114015900 1
1.0%
1121510700 1
1.0%
1126010200 2
2.0%
1129010300 1
1.0%
1130510300 1
1.0%
1135010500 1
1.0%
1144012000 1
1.0%
1162010100 1
1.0%
ValueCountFrequency (%)
5013010200 1
1.0%
4884025024 1
1.0%
4831032023 1
1.0%
4831010900 1
1.0%
4831010800 1
1.0%
4825010700 1
1.0%
4729025021 1
1.0%
4729011200 1
1.0%
4725010700 1
1.0%
4723011300 1
1.0%

hadm_cd
Real number (ℝ)

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5472239 × 109
Minimum1.1110615 × 109
Maximum5.013059 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:38.004641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110615 × 109
5-th percentile1.126059 × 109
Q12.7890634 × 109
median4.133539 × 109
Q34.4215521 × 109
95-th percentile4.8253538 × 109
Maximum5.013059 × 109
Range3.9019975 × 109
Interquartile range (IQR)1.6324888 × 109

Descriptive statistics

Standard deviation1.2191813 × 109
Coefficient of variation (CV)0.34370014
Kurtosis-0.35439447
Mean3.5472239 × 109
Median Absolute Deviation (MAD)5.501635 × 108
Skewness-0.97763779
Sum3.5472239 × 1011
Variance1.4864031 × 1018
MonotonicityNot monotonic
2023-12-10T19:14:38.348085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511151000 2
 
2.0%
4221058000 2
 
2.0%
4137057000 2
 
2.0%
1126059000 2
 
2.0%
4127353200 2
 
2.0%
2623052000 2
 
2.0%
2811058500 1
 
1.0%
4831058000 1
 
1.0%
4719053500 1
 
1.0%
4131052000 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
1111061500 1
1.0%
1114052000 1
1.0%
1114060500 1
1.0%
1121571000 1
1.0%
1126059000 2
2.0%
1129059000 1
1.0%
1130563500 1
1.0%
1135064000 1
1.0%
1144068000 1
1.0%
1162059500 1
1.0%
ValueCountFrequency (%)
5013059000 1
1.0%
4884025000 1
1.0%
4831058000 1
1.0%
4831057000 1
1.0%
4831032000 1
1.0%
4825055000 1
1.0%
4729055000 1
1.0%
4729025000 1
1.0%
4725052000 1
1.0%
4723054000 1
1.0%

rd_cd
Real number (ℝ)

MISSING 

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.5483502 × 1011
Minimum1.111021 × 1011
Maximum5.0130335 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:38.596531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111021 × 1011
5-th percentile1.1260412 × 1011
Q12.8127093 × 1011
median4.1335439 × 1011
Q34.4225325 × 1011
95-th percentile4.8259334 × 1011
Maximum5.0130335 × 1011
Range3.9020125 × 1011
Interquartile range (IQR)1.6098233 × 1011

Descriptive statistics

Standard deviation1.2260228 × 1011
Coefficient of variation (CV)0.34551912
Kurtosis-0.35899192
Mean3.5483502 × 1011
Median Absolute Deviation (MAD)5.5015727 × 1010
Skewness-0.98316973
Sum3.4773832 × 1013
Variance1.503132 × 1022
MonotonicityNot monotonic
2023-12-10T19:14:38.850461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117403124006 1
 
1.0%
472903000047 1
 
1.0%
483103337005 1
 
1.0%
471904724064 1
 
1.0%
413104388055 1
 
1.0%
414803206035 1
 
1.0%
472903313003 1
 
1.0%
448003262019 1
 
1.0%
413604391531 1
 
1.0%
281103149001 1
 
1.0%
Other values (88) 88
88.0%
(Missing) 2
 
2.0%
ValueCountFrequency (%)
111102100001 1
1.0%
111403101001 1
1.0%
111403101002 1
1.0%
112154112197 1
1.0%
112604118146 1
1.0%
112604118147 1
1.0%
112903107009 1
1.0%
113053005039 1
1.0%
113504130298 1
1.0%
114403113014 1
1.0%
ValueCountFrequency (%)
501303350224 1
1.0%
488403343022 1
1.0%
483104811091 1
1.0%
483103337047 1
1.0%
483103337005 1
1.0%
482503335032 1
1.0%
472903313003 1
1.0%
472903000047 1
1.0%
472503311074 1
1.0%
472304730382 1
1.0%

rd_nm
Text

MISSING 

Distinct96
Distinct (%)98.0%
Missing2
Missing (%)2.0%
Memory size932.0 B
2023-12-10T19:14:39.275560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.7653061
Min length3

Characters and Unicode

Total characters467
Distinct characters132
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)95.9%

Sample

1st row문정로
2nd row송내대로74번길
3rd row월드컵로
4th row엑스포로
5th row왕산로
ValueCountFrequency (%)
전주객사3길 2
 
2.0%
중앙로 2
 
2.0%
문정로 1
 
1.0%
거제중앙로 1
 
1.0%
구미중앙로13길 1
 
1.0%
동구릉로136번길 1
 
1.0%
방촌로 1
 
1.0%
강변동로 1
 
1.0%
조양로 1
 
1.0%
수레로661번안길 1
 
1.0%
Other values (86) 86
87.8%
2023-12-10T19:14:39.920303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
19.1%
35
 
7.5%
21
 
4.5%
20
 
4.3%
3 14
 
3.0%
11
 
2.4%
2 10
 
2.1%
1 10
 
2.1%
10
 
2.1%
6 9
 
1.9%
Other values (122) 238
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 394
84.4%
Decimal Number 73
 
15.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
89
22.6%
35
 
8.9%
21
 
5.3%
20
 
5.1%
11
 
2.8%
10
 
2.5%
9
 
2.3%
6
 
1.5%
6
 
1.5%
5
 
1.3%
Other values (112) 182
46.2%
Decimal Number
ValueCountFrequency (%)
3 14
19.2%
2 10
13.7%
1 10
13.7%
6 9
12.3%
7 7
9.6%
4 7
9.6%
0 5
 
6.8%
5 4
 
5.5%
9 4
 
5.5%
8 3
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 394
84.4%
Common 73
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
89
22.6%
35
 
8.9%
21
 
5.3%
20
 
5.1%
11
 
2.8%
10
 
2.5%
9
 
2.3%
6
 
1.5%
6
 
1.5%
5
 
1.3%
Other values (112) 182
46.2%
Common
ValueCountFrequency (%)
3 14
19.2%
2 10
13.7%
1 10
13.7%
6 9
12.3%
7 7
9.6%
4 7
9.6%
0 5
 
6.8%
5 4
 
5.5%
9 4
 
5.5%
8 3
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 394
84.4%
ASCII 73
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
89
22.6%
35
 
8.9%
21
 
5.3%
20
 
5.1%
11
 
2.8%
10
 
2.5%
9
 
2.3%
6
 
1.5%
6
 
1.5%
5
 
1.3%
Other values (112) 182
46.2%
ASCII
ValueCountFrequency (%)
3 14
19.2%
2 10
13.7%
1 10
13.7%
6 9
12.3%
7 7
9.6%
4 7
9.6%
0 5
 
6.8%
5 4
 
5.5%
9 4
 
5.5%
8 3
 
4.1%

bld_num
Text

MISSING 

Distinct80
Distinct (%)81.6%
Missing2
Missing (%)2.0%
Memory size932.0 B
2023-12-10T19:14:40.296407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.8061224
Min length1

Characters and Unicode

Total characters275
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

Unique66 ?
Unique (%)67.3%

Sample

1st row77
2nd row22
3rd row33
4th row75
5th row219-13
ValueCountFrequency (%)
26 3
 
3.1%
67 3
 
3.1%
12 3
 
3.1%
22 3
 
3.1%
38 2
 
2.0%
77 2
 
2.0%
83 2
 
2.0%
210 2
 
2.0%
47 2
 
2.0%
25 2
 
2.0%
Other values (70) 74
75.5%
2023-12-10T19:14:40.966081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 54
19.6%
2 48
17.5%
7 23
8.4%
3 21
 
7.6%
5 20
 
7.3%
9 19
 
6.9%
4 19
 
6.9%
8 19
 
6.9%
6 18
 
6.5%
0 17
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
93.8%
Dash Punctuation 17
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54
20.9%
2 48
18.6%
7 23
8.9%
3 21
 
8.1%
5 20
 
7.8%
9 19
 
7.4%
4 19
 
7.4%
8 19
 
7.4%
6 18
 
7.0%
0 17
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 275
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54
19.6%
2 48
17.5%
7 23
8.4%
3 21
 
7.6%
5 20
 
7.3%
9 19
 
6.9%
4 19
 
6.9%
8 19
 
6.9%
6 18
 
6.5%
0 17
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54
19.6%
2 48
17.5%
7 23
8.4%
3 21
 
7.6%
5 20
 
7.3%
9 19
 
6.9%
4 19
 
6.9%
8 19
 
6.9%
6 18
 
6.5%
0 17
 
6.2%

x
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.56357
Minimum126.40277
Maximum129.3628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:41.228807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40277
5-th percentile126.68131
Q1127.02453
median127.14901
Q3128.16331
95-th percentile129.08602
Maximum129.3628
Range2.9600251
Interquartile range (IQR)1.1387792

Descriptive statistics

Standard deviation0.81499776
Coefficient of variation (CV)0.006388954
Kurtosis-0.67745858
Mean127.56357
Median Absolute Deviation (MAD)0.29754176
Skewness0.84778169
Sum12756.357
Variance0.66422135
MonotonicityNot monotonic
2023-12-10T19:14:41.530311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.387989439949 1
 
1.0%
128.561705508508 1
 
1.0%
127.029830149174 1
 
1.0%
128.823321553347 1
 
1.0%
128.62377929094 1
 
1.0%
128.332654052116 1
 
1.0%
127.140551654476 1
 
1.0%
126.784097565862 1
 
1.0%
128.733404152256 1
 
1.0%
126.669628283525 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.402773668643 1
1.0%
126.465614454498 1
1.0%
126.508318986797 1
1.0%
126.629881627287 1
1.0%
126.669628283525 1
1.0%
126.681924013324 1
1.0%
126.683299947523 1
1.0%
126.697764847095 1
1.0%
126.755297477124 1
1.0%
126.784097565862 1
1.0%
ValueCountFrequency (%)
129.362798756983 1
1.0%
129.225883985609 1
1.0%
129.213223846732 1
1.0%
129.11537673361 1
1.0%
129.092796896126 1
1.0%
129.085665598687 1
1.0%
129.062743687391 1
1.0%
129.060675742304 1
1.0%
129.058074865351 1
1.0%
129.028341295572 1
1.0%

y
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.618793
Minimum33.245885
Maximum38.190817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:14:41.815407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.245885
5-th percentile34.886544
Q135.83817
median36.994029
Q337.483108
95-th percentile37.871685
Maximum38.190817
Range4.9449316
Interquartile range (IQR)1.6449386

Descriptive statistics

Standard deviation1.0432803
Coefficient of variation (CV)0.028490296
Kurtosis-0.37081485
Mean36.618793
Median Absolute Deviation (MAD)0.61281684
Skewness-0.66869053
Sum3661.8793
Variance1.0884337
MonotonicityNot monotonic
2023-12-10T19:14:42.197033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3475950596837 1
 
1.0%
35.9441475157073 1
 
1.0%
37.6423974140791 1
 
1.0%
35.9136507035683 1
 
1.0%
34.8887370992508 1
 
1.0%
36.1295590807456 1
 
1.0%
37.6118919334909 1
 
1.0%
37.8666541571417 1
 
1.0%
35.8347048272734 1
 
1.0%
36.6002508203422 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.2458850224847 1
1.0%
34.7306189708031 1
1.0%
34.8062545631822 1
1.0%
34.8120563222429 1
1.0%
34.8448809385076 1
1.0%
34.8887370992508 1
1.0%
34.8911974709103 1
1.0%
35.1045032750589 1
1.0%
35.1133344424085 1
1.0%
35.1277301103528 1
1.0%
ValueCountFrequency (%)
38.1908166230844 1
1.0%
38.1890808608622 1
1.0%
38.109652088372 1
1.0%
37.9739817587012 1
1.0%
37.9672612225078 1
1.0%
37.8666541571417 1
1.0%
37.6566994854042 1
1.0%
37.6523305955834 1
1.0%
37.6423974140791 1
1.0%
37.6191807618972 1
1.0%

grid_cd
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:14:42.727902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row다바899166
2nd row다사341436
3rd row다나076731
4th row라아948217
5th row라바593243
ValueCountFrequency (%)
다바899166 1
 
1.0%
다마677087 1
 
1.0%
마마194693 1
 
1.0%
마라026554 1
 
1.0%
라마749928 1
 
1.0%
다사682570 1
 
1.0%
다사370854 1
 
1.0%
마마113605 1
 
1.0%
다바257450 1
 
1.0%
다사800577 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:14:43.669365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 78
9.8%
5 73
 
9.1%
64
 
8.0%
9 63
 
7.9%
6 63
 
7.9%
7 60
 
7.5%
1 56
 
7.0%
8 54
 
6.8%
2 53
 
6.6%
0 52
 
6.5%
Other values (7) 184
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Other Letter 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 78
13.0%
5 73
12.2%
9 63
10.5%
6 63
10.5%
7 60
10.0%
1 56
9.3%
8 54
9.0%
2 53
8.8%
0 52
8.7%
3 48
8.0%
Other Letter
ValueCountFrequency (%)
64
32.0%
44
22.0%
35
17.5%
34
17.0%
18
 
9.0%
3
 
1.5%
2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
75.0%
Hangul 200
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 78
13.0%
5 73
12.2%
9 63
10.5%
6 63
10.5%
7 60
10.0%
1 56
9.3%
8 54
9.0%
2 53
8.8%
0 52
8.7%
3 48
8.0%
Hangul
ValueCountFrequency (%)
64
32.0%
44
22.0%
35
17.5%
34
17.0%
18
 
9.0%
3
 
1.5%
2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
75.0%
Hangul 200
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 78
13.0%
5 73
12.2%
9 63
10.5%
6 63
10.5%
7 60
10.0%
1 56
9.3%
8 54
9.0%
2 53
8.8%
0 52
8.7%
3 48
8.0%
Hangul
ValueCountFrequency (%)
64
32.0%
44
22.0%
35
17.5%
34
17.0%
18
 
9.0%
3
 
1.5%
2
 
1.0%

lst_updt_dt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210916172701
100 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210916172701
2nd row20210916172701
3rd row20210916172701
4th row20210916172701
5th row20210916172701

Common Values

ValueCountFrequency (%)
20210916172701 100
100.0%

Length

2023-12-10T19:14:43.930629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:44.108555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210916172701 100
100.0%

data_orgn
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KT 100
100.0%

Length

2023-12-10T19:14:44.290879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:44.475477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kt 100
100.0%

file_name
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_497_DMSTC_MCST_THEART_2021
100 

Length

Max length29
Median length29
Mean length29
Min length29

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_497_DMSTC_MCST_THEART_2021
2nd rowKC_497_DMSTC_MCST_THEART_2021
3rd rowKC_497_DMSTC_MCST_THEART_2021
4th rowKC_497_DMSTC_MCST_THEART_2021
5th rowKC_497_DMSTC_MCST_THEART_2021

Common Values

ValueCountFrequency (%)
KC_497_DMSTC_MCST_THEART_2021 100
100.0%

Length

2023-12-10T19:14:44.670136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:44.850050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_497_dmstc_mcst_theart_2021 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210916
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210916
2nd row20210916
3rd row20210916
4th row20210916
5th row20210916

Common Values

ValueCountFrequency (%)
20210916 100
100.0%

Length

2023-12-10T19:14:45.475888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:45.652588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210916 100
100.0%

Sample

idlclasmlsfcid_poipoi_nmbranch_nmsub_nmmcate_cdmcate_nmpnusido_nmsgg_nmbemd_nmri_nmbeonjibadm_cdhadm_cdrd_cdrd_nmbld_numxygrid_cdlst_updt_dtdata_orgnfile_namebase_ymd
0KCDMTPO21N000000001장소문화시설7232990메가박스대전점<NA>100212메가박스3017010600107460000대전광역시서구탄방동<NA>74630170106003017055500301703166027문정로77127.38798936.347595다바89916620210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
1KCDMTPO21N000001287장소문화시설21686628EDVD비디오방<NA><NA>92005비디오/DVD감상실4119010900104560000경기도부천시상동<NA>45641190109004119061000411904352299송내대로74번길22126.75529737.489845다사34143620210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
2KCDMTPO21N000000003장소문화시설7233044롯데시네마서귀포점<NA>100209롯데시네마5013010200109140000제주특별자치도서귀포시법환동<NA>91450130102005013059000501303350224월드컵로33126.50831933.245885다나07673120210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
3KCDMTPO21N000000004장소문화시설7430840아이맥스영화관<NA><NA>100202일반극장/영화관4221010800115450001강원도속초시조양동<NA>1545-142210108004221058000422103223008엑스포로75128.58259938.190817라아94821720210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
4KCDMTPO21N000000005장소문화시설10253648명성극장<NA><NA>100202일반극장/영화관4725010700100010004경상북도상주시남성동<NA>1-447250107004725052000472503311074왕산로219-13128.16214736.415135라바59324320210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
5KCDMTPO21N000000006장소문화시설11282700메가박스원주점<NA>100212메가박스4213011000108730000강원도원주시단계동<NA>87342130110004213059000421302219001서원대로165-3127.92976137.345028라사38027420210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
6KCDMTPO21N000000007장소문화시설16108373영보소극장<NA><NA>100202일반극장/영화관4721010100103770018경상북도영주시영주동<NA>377-1847210101004721055000472104727058구성로350번길24128.62424336.823845마바00270120210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
7KCDMTPO21N000001288장소문화시설21688285퀸비디오<NA><NA>92005비디오/DVD감상실3020011700102780002대전광역시유성구장대동<NA>278-230200117003020054000302004301354유성대로736번길19127.33490236.358711다바85117920210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
8KCDMTPO21N000000009장소문화시설17519188뮤즈DVD방<NA><NA>92005비디오/DVD감상실4113110100125150000경기도성남시 수정구신흥동<NA>251541131101004113153000411312179002산성대로269127.14694937.441178다사68738020210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
9KCDMTPO21N000000010장소문화시설18832436거제연육교자동차극장<NA><NA>100202일반극장/영화관4831032023100950001경상남도거제시동부면구천리95-148310320234831032000483104811091구천5길25128.6372134.812056마라04046920210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
idlclasmlsfcid_poipoi_nmbranch_nmsub_nmmcate_cdmcate_nmpnusido_nmsgg_nmbemd_nmri_nmbeonjibadm_cdhadm_cdrd_cdrd_nmbld_numxygrid_cdlst_updt_dtdata_orgnfile_namebase_ymd
90KCDMTPO21N000000091장소문화시설11405660메가박스상봉점<NA>100212메가박스1126010200101300003서울특별시중랑구상봉동<NA>130-311260102001126059000112604118146망우로30길3127.07471937.593174다사62454920210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
91KCDMTPO21N000000092장소문화시설14106543롯데시네마서면점<NA>100209롯데시네마2623010200106680001부산광역시부산진구전포동<NA>668-126230102002623060000262303129010동천로92129.06274435.157235마라42385720210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
92KCDMTPO21N000000093장소문화시설16092627남대문극장<NA><NA>100202일반극장/영화관1114011700100030003서울특별시중구남대문로4가<NA>3-311140117001114052000111403101001남대문시장길25-8126.97748537.560552다사53851320210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
93KCDMTPO21N000000094장소문화시설16367919메가박스첨단점<NA>100212메가박스2920011600106880004광주광역시광산구쌍암동<NA>688-429200116002920062400292003162090앰코로35126.85240135.221232다라41091920210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
94KCDMTPO21N000000095장소문화시설16399088CGV인천연수점<NA>100208CGV2818510500109260000인천광역시연수구동춘동<NA>92628185105002818579500281852008006청능대로210126.683337.406355다사27734420210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
95KCDMTPO21N000000096장소문화시설16507786롯데시네마서청주점<NA>100209롯데시네마4311313800108110000충청북도청주시 흥덕구비하동<NA>811431131380043113760004311330140652순환로1004127.42166236.644887다바92949620210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
96KCDMTPO21N000000097장소문화시설16511687롯데시네마합정점<NA>100209롯데시네마1144012000104900000서울특별시마포구서교동<NA>49011440120001144068000114403113014양화로45126.91349137.550373다사48150220210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
97KCDMTPO21N000000098장소문화시설17966879조이앤시네마<NA><NA>100202일반극장/영화관4511111700102880002전라북도전주시 완산구고사동<NA>288-245111117004511151000451114598335전주객사3길74-25127.14228235.821013다마67658320210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
98KCDMTPO21N000000099장소문화시설18131957씨네Q충주연수점<NA>100202일반극장/영화관4313011800113250000충청북도충주시연수동<NA>132543130118004313063000431302238004번영대로211127.94112536.987493라바39287720210916172701KTKC_497_DMSTC_MCST_THEART_202120210916
99KCDMTPO21N000000100장소문화시설18438025케이지블루풋살존<NA><NA>92005비디오/DVD감상실4111514100110330014경기도수원시 팔달구인계동<NA>1033-1441115141004111573000411154328227인계로108번길39-17127.02977337.26594다사58318620210916172701KTKC_497_DMSTC_MCST_THEART_202120210916