Overview

Dataset statistics

Number of variables27
Number of observations100
Missing cells444
Missing cells (%)16.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory228.3 B

Variable types

Text10
Categorical9
Numeric7
Unsupported1

Alerts

lclas has constant value ""Constant
mlsfc has constant value ""Constant
branch_nm 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
mcate_cd is highly imbalanced (79.7%)Imbalance
mcate_nm is highly imbalanced (79.7%)Imbalance
branch_nm has 99 (99.0%) missing valuesMissing
sub_nm has 100 (100.0%) missing valuesMissing
ri_nm has 72 (72.0%) missing valuesMissing
rd_cd has 57 (57.0%) missing valuesMissing
rd_nm has 57 (57.0%) missing valuesMissing
bld_num has 57 (57.0%) missing valuesMissing
id has unique valuesUnique
id_poi 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 10:16:33.990386
Analysis finished2023-12-10 10:16:34.675227
Duration0.68 seconds
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
2023-12-10T19:16:34.874188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters16
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 rowKCDPCPO21N000000001
2nd rowKCDPCPO21N000008640
3rd rowKCDPCPO21N000000003
4th rowKCDPCPO21N000000004
5th rowKCDPCPO21N000000005
ValueCountFrequency (%)
kcdpcpo21n000000001 1
 
1.0%
kcdpcpo21n000000063 1
 
1.0%
kcdpcpo21n000000074 1
 
1.0%
kcdpcpo21n000000073 1
 
1.0%
kcdpcpo21n000000072 1
 
1.0%
kcdpcpo21n000000071 1
 
1.0%
kcdpcpo21n000000070 1
 
1.0%
kcdpcpo21n000000069 1
 
1.0%
kcdpcpo21n000000068 1
 
1.0%
kcdpcpo21n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:16:35.353863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 712
37.5%
C 200
 
10.5%
P 200
 
10.5%
1 122
 
6.4%
2 119
 
6.3%
K 100
 
5.3%
D 100
 
5.3%
O 100
 
5.3%
N 100
 
5.3%
4 23
 
1.2%
Other values (6) 124
 
6.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 712
64.7%
1 122
 
11.1%
2 119
 
10.8%
4 23
 
2.1%
6 23
 
2.1%
8 22
 
2.0%
5 20
 
1.8%
7 20
 
1.8%
9 20
 
1.8%
3 19
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 200
25.0%
P 200
25.0%
K 100
12.5%
D 100
12.5%
O 100
12.5%
N 100
12.5%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 712
64.7%
1 122
 
11.1%
2 119
 
10.8%
4 23
 
2.1%
6 23
 
2.1%
8 22
 
2.0%
5 20
 
1.8%
7 20
 
1.8%
9 20
 
1.8%
3 19
 
1.7%
Latin
ValueCountFrequency (%)
C 200
25.0%
P 200
25.0%
K 100
12.5%
D 100
12.5%
O 100
12.5%
N 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 712
37.5%
C 200
 
10.5%
P 200
 
10.5%
1 122
 
6.4%
2 119
 
6.3%
K 100
 
5.3%
D 100
 
5.3%
O 100
 
5.3%
N 100
 
5.3%
4 23
 
1.2%
Other values (6) 124
 
6.5%

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

2023-12-10T19:16:35.587838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:35.712780image/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
관광지
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관광지 100
100.0%

Length

2023-12-10T19:16:35.839831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:35.963734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광지 100
100.0%

id_poi
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12068860
Minimum213411
Maximum22112595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:36.137974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum213411
5-th percentile539989.05
Q16818141
median16490326
Q316496300
95-th percentile18239225
Maximum22112595
Range21899184
Interquartile range (IQR)9678158.8

Descriptive statistics

Standard deviation6736821.6
Coefficient of variation (CV)0.55819869
Kurtosis-0.93081122
Mean12068860
Median Absolute Deviation (MAD)2034362
Skewness-0.81587469
Sum1.206886 × 109
Variance4.5384766 × 1013
MonotonicityNot monotonic
2023-12-10T19:16:36.321908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213411 1
 
1.0%
16491844 1
 
1.0%
16495102 1
 
1.0%
16493159 1
 
1.0%
16493088 1
 
1.0%
16493028 1
 
1.0%
16492880 1
 
1.0%
16492474 1
 
1.0%
16492247 1
 
1.0%
16492138 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
213411 1
1.0%
231585 1
1.0%
269048 1
1.0%
539477 1
1.0%
539762 1
1.0%
540001 1
1.0%
540220 1
1.0%
540355 1
1.0%
540375 1
1.0%
540446 1
1.0%
ValueCountFrequency (%)
22112595 1
1.0%
20664022 1
1.0%
20511711 1
1.0%
18449065 1
1.0%
18266209 1
1.0%
18237805 1
1.0%
18103646 1
1.0%
18048925 1
1.0%
18004909 1
1.0%
17847103 1
1.0%

poi_nm
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:36.675133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.83
Min length4

Characters and Unicode

Total characters583
Distinct characters181
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)95.0%

Sample

1st row근린공원
2nd row공원아산국가산단
3rd row신월어린이공원
4th row까치공원
5th row수양관공원
ValueCountFrequency (%)
근린공원 3
 
3.0%
소망어린이공원 2
 
2.0%
상서면체육공원 1
 
1.0%
나래놀이공원 1
 
1.0%
화원체육공원 1
 
1.0%
햇님공원 1
 
1.0%
푸른길공원 1
 
1.0%
죽곡1어린이공원 1
 
1.0%
의서어린이공원 1
 
1.0%
우산제7호어린이공원 1
 
1.0%
Other values (87) 87
87.0%
2023-12-10T19:16:37.198416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 97
 
16.6%
공 95
 
16.3%
린 21
 
3.6%
이 20
 
3.4%
어 18
 
3.1%
산 11
 
1.9%
체 9
 
1.5%
육 9
 
1.5%
리 7
 
1.2%
천 7
 
1.2%
Other values (171) 289
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 574
98.5%
Decimal Number 8
 
1.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 97
 
16.9%
공 95
 
16.6%
린 21
 
3.7%
이 20
 
3.5%
어 18
 
3.1%
산 11
 
1.9%
체 9
 
1.6%
육 9
 
1.6%
리 7
 
1.2%
천 7
 
1.2%
Other values (165) 280
48.8%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
7 1
 
12.5%
8 1
 
12.5%
5 1
 
12.5%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 574
98.5%
Common 9
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 97
 
16.9%
공 95
 
16.6%
린 21
 
3.7%
이 20
 
3.5%
어 18
 
3.1%
산 11
 
1.9%
체 9
 
1.6%
육 9
 
1.6%
리 7
 
1.2%
천 7
 
1.2%
Other values (165) 280
48.8%
Common
ValueCountFrequency (%)
1 4
44.4%
7 1
 
11.1%
8 1
 
11.1%
. 1
 
11.1%
5 1
 
11.1%
2 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 574
98.5%
ASCII 9
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 97
 
16.9%
공 95
 
16.6%
린 21
 
3.7%
이 20
 
3.5%
어 18
 
3.1%
산 11
 
1.9%
체 9
 
1.6%
육 9
 
1.6%
리 7
 
1.2%
천 7
 
1.2%
Other values (165) 280
48.8%
ASCII
ValueCountFrequency (%)
1 4
44.4%
7 1
 
11.1%
8 1
 
11.1%
. 1
 
11.1%
5 1
 
11.1%
2 1
 
11.1%

branch_nm
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing99
Missing (%)99.0%
Memory size932.0 B
2023-12-10T19:16:37.381557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
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

Unique1 ?
Unique (%)100.0%

Sample

1st row거문백도지구
ValueCountFrequency (%)
거문백도지구 1
100.0%
2023-12-10T19:16:37.755917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
거 1
16.7%
문 1
16.7%
백 1
16.7%
도 1
16.7%
지 1
16.7%
구 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
거 1
16.7%
문 1
16.7%
백 1
16.7%
도 1
16.7%
지 1
16.7%
구 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
거 1
16.7%
문 1
16.7%
백 1
16.7%
도 1
16.7%
지 1
16.7%
구 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
거 1
16.7%
문 1
16.7%
백 1
16.7%
도 1
16.7%
지 1
16.7%
구 1
16.7%

sub_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

mcate_cd
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50705
95 
50703
 
4
50701
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row50705
2nd row50705
3rd row50705
4th row50705
5th row50705

Common Values

ValueCountFrequency (%)
50705 95
95.0%
50703 4
 
4.0%
50701 1
 
1.0%

Length

2023-12-10T19:16:37.946469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:38.073995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50705 95
95.0%
50703 4
 
4.0%
50701 1
 
1.0%

mcate_nm
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
지역근린공원
95 
대형시민/도심공원
 
4
국립공원
 
1

Length

Max length9
Median length6
Mean length6.1
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역근린공원 95
95.0%
대형시민/도심공원 4
 
4.0%
국립공원 1
 
1.0%

Length

2023-12-10T19:16:38.227914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:38.371548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역근린공원 95
95.0%
대형시민/도심공원 4
 
4.0%
국립공원 1
 
1.0%

pnu
Real number (ℝ)

Distinct99
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean3.3076239 × 1018
Minimum1.1140167 × 1018
Maximum4.885035 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:38.505440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1140167 × 1018
5-th percentile1.1350105 × 1018
Q12.7230122 × 1018
median3.1710265 × 1018
Q34.2110217 × 1018
95-th percentile4.7152132 × 1018
Maximum4.885035 × 1018
Range3.7710183 × 1018
Interquartile range (IQR)1.4880095 × 1018

Descriptive statistics

Standard deviation1.1934496 × 1018
Coefficient of variation (CV)0.36081782
Kurtosis-0.78134844
Mean3.3076239 × 1018
Median Absolute Deviation (MAD)9.6998388 × 1017
Skewness-0.62830414
Sum-4.5866255 × 1018
Variance1.4243221 × 1036
MonotonicityNot monotonic
2023-12-10T19:16:38.700691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3114010600115530240 1
 
1.0%
4119011000101349888 1
 
1.0%
2671025022103249920 1
 
1.0%
2771025023107440128 1
 
1.0%
1121510700100450048 1
 
1.0%
2911010900107299840 1
 
1.0%
2771025628115160064 1
 
1.0%
4115010100104820224 1
 
1.0%
2920010900110450176 1
 
1.0%
4211012300111569920 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
1114016700100150016 1
1.0%
1121510700100450048 1
1.0%
1126010100100419968 1
1.0%
1130510200102300032 1
1.0%
1135010500107200000 1
1.0%
1135010500112930048 1
1.0%
1135010600105080064 1
1.0%
1138010400104360064 1
1.0%
1138010700100850048 1
1.0%
1147010300102400000 1
1.0%
ValueCountFrequency (%)
4885035021105829888 1
1.0%
4812914500101409792 1
1.0%
4719031026100689920 1
1.0%
4719010300100769792 1
1.0%
4717036022101409792 1
1.0%
4715010700100689920 1
1.0%
4713012400110550016 1
1.0%
4713012400110210048 1
1.0%
4711125026101690368 1
1.0%
4681025025104369664 1
1.0%

sido_nm
Categorical

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
23 
서울특별시
16 
대구광역시
9 
부산광역시
8 
경상북도
7 
Other values (9)
37 

Length

Max length5
Median length4.5
Mean length4.23
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산광역시
2nd row충청남도
3rd row서울특별시
4th row서울특별시
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 23
23.0%
서울특별시 16
16.0%
대구광역시 9
 
9.0%
부산광역시 8
 
8.0%
경상북도 7
 
7.0%
울산광역시 6
 
6.0%
인천광역시 6
 
6.0%
광주광역시 5
 
5.0%
전라북도 5
 
5.0%
충청남도 4
 
4.0%
Other values (4) 11
11.0%

Length

2023-12-10T19:16:38.910882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 23
23.0%
서울특별시 16
16.0%
대구광역시 9
 
9.0%
부산광역시 8
 
8.0%
경상북도 7
 
7.0%
울산광역시 6
 
6.0%
인천광역시 6
 
6.0%
광주광역시 5
 
5.0%
전라북도 5
 
5.0%
충청남도 4
 
4.0%
Other values (4) 11
11.0%

sgg_nm
Text

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:39.234671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.54
Min length2

Characters and Unicode

Total characters354
Distinct characters72
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

Unique34 ?
Unique (%)34.0%

Sample

1st row남구
2nd row당진시
3rd row양천구
4th row강남구
5th row용인시 처인구
ValueCountFrequency (%)
부천시 4
 
3.5%
전주시 4
 
3.5%
동구 3
 
2.6%
고양시 3
 
2.6%
남구 3
 
2.6%
울주군 3
 
2.6%
노원구 3
 
2.6%
달서구 3
 
2.6%
완산구 3
 
2.6%
광산구 3
 
2.6%
Other values (59) 83
72.2%
2023-12-10T19:16:39.787784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 60
 
16.9%
시 44
 
12.4%
15
 
4.2%
주 15
 
4.2%
군 15
 
4.2%
양 11
 
3.1%
천 10
 
2.8%
남 9
 
2.5%
산 9
 
2.5%
동 8
 
2.3%
Other values (62) 158
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 339
95.8%
Space Separator 15
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 60
 
17.7%
시 44
 
13.0%
주 15
 
4.4%
군 15
 
4.4%
양 11
 
3.2%
천 10
 
2.9%
남 9
 
2.7%
산 9
 
2.7%
동 8
 
2.4%
부 8
 
2.4%
Other values (61) 150
44.2%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 339
95.8%
Common 15
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 60
 
17.7%
시 44
 
13.0%
주 15
 
4.4%
군 15
 
4.4%
양 11
 
3.2%
천 10
 
2.9%
남 9
 
2.7%
산 9
 
2.7%
동 8
 
2.4%
부 8
 
2.4%
Other values (61) 150
44.2%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 339
95.8%
ASCII 15
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 60
 
17.7%
시 44
 
13.0%
주 15
 
4.4%
군 15
 
4.4%
양 11
 
3.2%
천 10
 
2.9%
남 9
 
2.7%
산 9
 
2.7%
동 8
 
2.4%
부 8
 
2.4%
Other values (61) 150
44.2%
ASCII
ValueCountFrequency (%)
15
100.0%

bemd_nm
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:40.169516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1
Min length2

Characters and Unicode

Total characters310
Distinct characters111
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

Unique85 ?
Unique (%)85.0%

Sample

1st row삼산동
2nd row송악읍
3rd row신월동
4th row도곡동
5th row양지면
ValueCountFrequency (%)
삼산동 3
 
3.0%
의정부동 2
 
2.0%
소사본동 2
 
2.0%
황성동 2
 
2.0%
상계동 2
 
2.0%
상인동 2
 
2.0%
도곡동 2
 
2.0%
좌동 1
 
1.0%
기장읍 1
 
1.0%
화원읍 1
 
1.0%
Other values (82) 82
82.0%
2023-12-10T19:16:40.648752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 74
23.9%
읍 19
 
6.1%
면 11
 
3.5%
산 8
 
2.6%
정 7
 
2.3%
삼 6
 
1.9%
상 6
 
1.9%
가 5
 
1.6%
계 5
 
1.6%
성 5
 
1.6%
Other values (101) 164
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
98.7%
Decimal Number 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 74
24.2%
읍 19
 
6.2%
면 11
 
3.6%
산 8
 
2.6%
정 7
 
2.3%
삼 6
 
2.0%
상 6
 
2.0%
가 5
 
1.6%
계 5
 
1.6%
성 5
 
1.6%
Other values (99) 160
52.3%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
5 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
98.7%
Common 4
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 74
24.2%
읍 19
 
6.2%
면 11
 
3.6%
산 8
 
2.6%
정 7
 
2.3%
삼 6
 
2.0%
상 6
 
2.0%
가 5
 
1.6%
계 5
 
1.6%
성 5
 
1.6%
Other values (99) 160
52.3%
Common
ValueCountFrequency (%)
2 3
75.0%
5 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
98.7%
ASCII 4
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 74
24.2%
읍 19
 
6.2%
면 11
 
3.6%
산 8
 
2.6%
정 7
 
2.3%
삼 6
 
2.0%
상 6
 
2.0%
가 5
 
1.6%
계 5
 
1.6%
성 5
 
1.6%
Other values (99) 160
52.3%
ASCII
ValueCountFrequency (%)
2 3
75.0%
5 1
 
25.0%

ri_nm
Text

MISSING 

Distinct27
Distinct (%)96.4%
Missing72
Missing (%)72.0%
Memory size932.0 B
2023-12-10T19:16:40.847588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9285714
Min length2

Characters and Unicode

Total characters82
Distinct characters43
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

Unique26 ?
Unique (%)92.9%

Sample

1st row한진리
2nd row추계리
3rd row무등리
4th row송산리
5th row능안리
ValueCountFrequency (%)
송산리 2
 
7.1%
성산리 1
 
3.6%
매곡리 1
 
3.6%
동도리 1
 
3.6%
남포리 1
 
3.6%
범아리 1
 
3.6%
굴화리 1
 
3.6%
신경리 1
 
3.6%
목리 1
 
3.6%
무릉리 1
 
3.6%
Other values (17) 17
60.7%
2023-12-10T19:16:41.142967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 28
34.1%
산 4
 
4.9%
송 3
 
3.7%
교 2
 
2.4%
신 2
 
2.4%
화 2
 
2.4%
동 2
 
2.4%
곡 2
 
2.4%
매 2
 
2.4%
무 2
 
2.4%
Other values (33) 33
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 28
34.1%
산 4
 
4.9%
송 3
 
3.7%
교 2
 
2.4%
신 2
 
2.4%
화 2
 
2.4%
동 2
 
2.4%
곡 2
 
2.4%
매 2
 
2.4%
무 2
 
2.4%
Other values (33) 33
40.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 28
34.1%
산 4
 
4.9%
송 3
 
3.7%
교 2
 
2.4%
신 2
 
2.4%
화 2
 
2.4%
동 2
 
2.4%
곡 2
 
2.4%
매 2
 
2.4%
무 2
 
2.4%
Other values (33) 33
40.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 28
34.1%
산 4
 
4.9%
송 3
 
3.7%
교 2
 
2.4%
신 2
 
2.4%
화 2
 
2.4%
동 2
 
2.4%
곡 2
 
2.4%
매 2
 
2.4%
무 2
 
2.4%
Other values (33) 33
40.2%

beonji
Text

Distinct98
Distinct (%)99.0%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:16:41.382868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.4141414
Min length2

Characters and Unicode

Total characters437
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row1553-1
2nd row404
3rd row240
4th row455
5th row214-4
ValueCountFrequency (%)
720 2
 
2.0%
707 1
 
1.0%
45-3 1
 
1.0%
730 1
 
1.0%
1516-2 1
 
1.0%
482 1
 
1.0%
1045-2 1
 
1.0%
1157 1
 
1.0%
1452 1
 
1.0%
556-33 1
 
1.0%
Other values (88) 88
88.9%
2023-12-10T19:16:42.057912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 78
17.8%
- 54
12.4%
2 51
11.7%
5 44
10.1%
4 41
9.4%
3 41
9.4%
0 27
 
6.2%
8 27
 
6.2%
7 26
 
5.9%
6 21
 
4.8%
Other values (2) 27
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 376
86.0%
Dash Punctuation 54
 
12.4%
Other Letter 7
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 78
20.7%
2 51
13.6%
5 44
11.7%
4 41
10.9%
3 41
10.9%
0 27
 
7.2%
8 27
 
7.2%
7 26
 
6.9%
6 21
 
5.6%
9 20
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Other Letter
ValueCountFrequency (%)
산 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 430
98.4%
Hangul 7
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 78
18.1%
- 54
12.6%
2 51
11.9%
5 44
10.2%
4 41
9.5%
3 41
9.5%
0 27
 
6.3%
8 27
 
6.3%
7 26
 
6.0%
6 21
 
4.9%
Hangul
ValueCountFrequency (%)
산 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430
98.4%
Hangul 7
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 78
18.1%
- 54
12.6%
2 51
11.9%
5 44
10.2%
4 41
9.5%
3 41
9.5%
0 27
 
6.3%
8 27
 
6.3%
7 26
 
6.0%
6 21
 
4.9%
Hangul
ValueCountFrequency (%)
산 7
100.0%

badm_cd
Real number (ℝ)

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.320678 × 109
Minimum1.1140167 × 109
Maximum4.885035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:42.222884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1140167 × 109
5-th percentile1.1350105 × 109
Q12.7230123 × 109
median3.6422684 × 109
Q34.2115325 × 109
95-th percentile4.715112 × 109
Maximum4.885035 × 109
Range3.7710183 × 109
Interquartile range (IQR)1.4885202 × 109

Descriptive statistics

Standard deviation1.194561 × 109
Coefficient of variation (CV)0.35973406
Kurtosis-0.7699766
Mean3.320678 × 109
Median Absolute Deviation (MAD)8.538002 × 108
Skewness-0.64009873
Sum3.320678 × 1011
Variance1.426976 × 1018
MonotonicityNot monotonic
2023-12-10T19:16:42.398269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1135010500 2
 
2.0%
1168011800 2
 
2.0%
4115010100 2
 
2.0%
2729011500 2
 
2.0%
4119011000 2
 
2.0%
4713012400 2
 
2.0%
2823710500 2
 
2.0%
4211012300 1
 
1.0%
2671025022 1
 
1.0%
2771025023 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
1114016700 1
1.0%
1121510700 1
1.0%
1126010100 1
1.0%
1130510200 1
1.0%
1135010500 2
2.0%
1135010600 1
1.0%
1138010400 1
1.0%
1138010700 1
1.0%
1147010300 1
1.0%
1156010100 1
1.0%
ValueCountFrequency (%)
4885035021 1
1.0%
4812914500 1
1.0%
4719031026 1
1.0%
4719010300 1
1.0%
4717036022 1
1.0%
4715010700 1
1.0%
4713012400 2
2.0%
4711125026 1
1.0%
4681025027 1
1.0%
4615012300 1
1.0%

hadm_cd
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3207139 × 109
Minimum1.114052 × 109
Maximum4.885035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:42.589775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.114052 × 109
5-th percentile1.1350663 × 109
Q12.7230744 × 109
median3.6422918 × 109
Q34.2115588 × 109
95-th percentile4.7151527 × 109
Maximum4.885035 × 109
Range3.770983 × 109
Interquartile range (IQR)1.4884844 × 109

Descriptive statistics

Standard deviation1.1945522 × 109
Coefficient of variation (CV)0.35972753
Kurtosis-0.76997129
Mean3.3207139 × 109
Median Absolute Deviation (MAD)8.537964 × 108
Skewness-0.64010189
Sum3.3207139 × 1011
Variance1.426955 × 1018
MonotonicityNot monotonic
2023-12-10T19:16:42.793251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2729062500 2
 
2.0%
4115052000 2
 
2.0%
4119075000 2
 
2.0%
4713062100 2
 
2.0%
3114057000 1
 
1.0%
2635055200 1
 
1.0%
2671025000 1
 
1.0%
2771025000 1
 
1.0%
1121571000 1
 
1.0%
2911056000 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
1114052000 1
1.0%
1121571000 1
1.0%
1126057500 1
1.0%
1130560300 1
1.0%
1135062500 1
1.0%
1135066500 1
1.0%
1135069500 1
1.0%
1138055100 1
1.0%
1138058000 1
1.0%
1147056000 1
1.0%
ValueCountFrequency (%)
4885035000 1
1.0%
4812962000 1
1.0%
4719055100 1
1.0%
4719031000 1
1.0%
4717036000 1
1.0%
4715053600 1
1.0%
4713062100 2
2.0%
4711125000 1
1.0%
4681025000 1
1.0%
4615063500 1
1.0%

rd_cd
Real number (ℝ)

MISSING 

Distinct43
Distinct (%)100.0%
Missing57
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean3.1339876 × 1011
Minimum1.114041 × 1011
Maximum4.8129479 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:42.968186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.114041 × 1011
5-th percentile1.1264912 × 1011
Q12.6230257 × 1011
median2.9200316 × 1011
Q34.319038 × 1011
95-th percentile4.7168472 × 1011
Maximum4.8129479 × 1011
Range3.6989069 × 1011
Interquartile range (IQR)1.6960124 × 1011

Descriptive statistics

Standard deviation1.3331917 × 1011
Coefficient of variation (CV)0.4253979
Kurtosis-1.2759435
Mean3.1339876 × 1011
Median Absolute Deviation (MAD)1.2909985 × 1011
Skewness-0.36425708
Sum1.3476147 × 1013
Variance1.7774002 × 1022
MonotonicityNot monotonic
2023-12-10T19:16:43.233958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
116804166169 1
 
1.0%
277103148002 1
 
1.0%
265304217189 1
 
1.0%
282603156008 1
 
1.0%
317104319635 1
 
1.0%
113054124552 1
 
1.0%
451114598338 1
 
1.0%
471903308035 1
 
1.0%
451134601241 1
 
1.0%
263503133039 1
 
1.0%
Other values (33) 33
33.0%
(Missing) 57
57.0%
ValueCountFrequency (%)
111404103276 1
1.0%
112154112312 1
1.0%
112604118036 1
1.0%
113054124552 1
1.0%
113504130199 1
1.0%
113804133046 1
1.0%
113804133192 1
1.0%
115604154835 1
1.0%
115904160388 1
1.0%
116804166169 1
1.0%
ValueCountFrequency (%)
481294793467 1
1.0%
471903308035 1
1.0%
471704721393 1
1.0%
471504718171 1
1.0%
471113000101 1
1.0%
468103293010 1
1.0%
461504649571 1
1.0%
451404608335 1
1.0%
451134601241 1
1.0%
451114598338 1
1.0%

rd_nm
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing57
Missing (%)57.0%
Memory size932.0 B
2023-12-10T19:16:43.521094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.255814
Min length3

Characters and Unicode

Total characters226
Distinct characters87
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

Unique43 ?
Unique (%)100.0%

Sample

1st row고대공단1길
2nd row논현로26길
3rd row구암로
4th row번영로
5th row아나지로307번길
ValueCountFrequency (%)
고대공단1길 1
 
2.3%
좌동순환로 1
 
2.3%
은평로11길 1
 
2.3%
가창로 1
 
2.3%
백양대로950번나길 1
 
2.3%
간촌로 1
 
2.3%
신온1길 1
 
2.3%
한천로115길 1
 
2.3%
중산10길 1
 
2.3%
송동로 1
 
2.3%
Other values (33) 33
76.7%
2023-12-10T19:16:44.030592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 33
 
14.6%
길 25
 
11.1%
1 15
 
6.6%
2 7
 
3.1%
대 7
 
3.1%
번 7
 
3.1%
동 6
 
2.7%
7 4
 
1.8%
산 4
 
1.8%
5 4
 
1.8%
Other values (77) 114
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 182
80.5%
Decimal Number 44
 
19.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 33
 
18.1%
길 25
 
13.7%
대 7
 
3.8%
번 7
 
3.8%
동 6
 
3.3%
산 4
 
2.2%
해 3
 
1.6%
중 3
 
1.6%
원 3
 
1.6%
공 3
 
1.6%
Other values (68) 88
48.4%
Decimal Number
ValueCountFrequency (%)
1 15
34.1%
2 7
15.9%
7 4
 
9.1%
5 4
 
9.1%
0 4
 
9.1%
6 3
 
6.8%
4 3
 
6.8%
9 2
 
4.5%
3 2
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
80.5%
Common 44
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 33
 
18.1%
길 25
 
13.7%
대 7
 
3.8%
번 7
 
3.8%
동 6
 
3.3%
산 4
 
2.2%
해 3
 
1.6%
중 3
 
1.6%
원 3
 
1.6%
공 3
 
1.6%
Other values (68) 88
48.4%
Common
ValueCountFrequency (%)
1 15
34.1%
2 7
15.9%
7 4
 
9.1%
5 4
 
9.1%
0 4
 
9.1%
6 3
 
6.8%
4 3
 
6.8%
9 2
 
4.5%
3 2
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
80.5%
ASCII 44
 
19.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 33
 
18.1%
길 25
 
13.7%
대 7
 
3.8%
번 7
 
3.8%
동 6
 
3.3%
산 4
 
2.2%
해 3
 
1.6%
중 3
 
1.6%
원 3
 
1.6%
공 3
 
1.6%
Other values (68) 88
48.4%
ASCII
ValueCountFrequency (%)
1 15
34.1%
2 7
15.9%
7 4
 
9.1%
5 4
 
9.1%
0 4
 
9.1%
6 3
 
6.8%
4 3
 
6.8%
9 2
 
4.5%
3 2
 
4.5%

bld_num
Text

MISSING 

Distinct41
Distinct (%)95.3%
Missing57
Missing (%)57.0%
Memory size932.0 B
2023-12-10T19:16:44.315680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.1627907
Min length1

Characters and Unicode

Total characters136
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

Unique39 ?
Unique (%)90.7%

Sample

1st row10-6
2nd row34
3rd row47
4th row407
5th row1
ValueCountFrequency (%)
33 2
 
4.7%
20 2
 
4.7%
4376-30 1
 
2.3%
3-12 1
 
2.3%
10 1
 
2.3%
16-3 1
 
2.3%
97-73 1
 
2.3%
20-47 1
 
2.3%
604 1
 
2.3%
94 1
 
2.3%
Other values (31) 31
72.1%
2023-12-10T19:16:44.759185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 24
17.6%
3 21
15.4%
2 16
11.8%
0 15
11.0%
- 15
11.0%
4 13
9.6%
7 9
 
6.6%
6 9
 
6.6%
9 6
 
4.4%
5 5
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
89.0%
Dash Punctuation 15
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24
19.8%
3 21
17.4%
2 16
13.2%
0 15
12.4%
4 13
10.7%
7 9
 
7.4%
6 9
 
7.4%
9 6
 
5.0%
5 5
 
4.1%
8 3
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24
17.6%
3 21
15.4%
2 16
11.8%
0 15
11.0%
- 15
11.0%
4 13
9.6%
7 9
 
6.6%
6 9
 
6.6%
9 6
 
4.4%
5 5
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24
17.6%
3 21
15.4%
2 16
11.8%
0 15
11.0%
- 15
11.0%
4 13
9.6%
7 9
 
6.6%
6 9
 
6.6%
9 6
 
4.4%
5 5
 
3.7%

x
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.59729
Minimum126.67848
Maximum129.54561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:44.978879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.67848
5-th percentile126.73357
Q1126.87721
median127.10486
Q3128.53906
95-th percentile129.26333
Maximum129.54561
Range2.8671316
Interquartile range (IQR)1.6618487

Descriptive statistics

Standard deviation0.92188668
Coefficient of variation (CV)0.0072249706
Kurtosis-0.95027404
Mean127.59729
Median Absolute Deviation (MAD)0.34402279
Skewness0.81232349
Sum12759.729
Variance0.84987506
MonotonicityNot monotonic
2023-12-10T19:16:45.177089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.341120396096 1
 
1.0%
127.035484342399 1
 
1.0%
126.916473733274 1
 
1.0%
129.213024519709 1
 
1.0%
128.475961462037 1
 
1.0%
127.066882375642 1
 
1.0%
126.91600685842 1
 
1.0%
128.464117748794 1
 
1.0%
127.038401599695 1
 
1.0%
126.811445138281 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.678480796996 1
1.0%
126.684560984345 1
1.0%
126.688147810538 1
1.0%
126.721177063113 1
1.0%
126.725748547418 1
1.0%
126.733977840697 1
1.0%
126.738380804615 1
1.0%
126.745983570022 1
1.0%
126.757275653902 1
1.0%
126.758205150338 1
1.0%
ValueCountFrequency (%)
129.545612352071 1
1.0%
129.411794634947 1
1.0%
129.341120396096 1
1.0%
129.313168293359 1
1.0%
129.311731743944 1
1.0%
129.260782409893 1
1.0%
129.213024519709 1
1.0%
129.209489526441 1
1.0%
129.20944121608 1
1.0%
129.181047872576 1
1.0%

y
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.586082
Minimum34.058316
Maximum38.200954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:16:45.347620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.058316
5-th percentile35.113822
Q135.785679
median36.886275
Q337.521127
95-th percentile37.746184
Maximum38.200954
Range4.1426378
Interquartile range (IQR)1.7354482

Descriptive statistics

Standard deviation1.0419302
Coefficient of variation (CV)0.028478868
Kurtosis-1.3277724
Mean36.586082
Median Absolute Deviation (MAD)0.8069813
Skewness-0.35105073
Sum3658.6082
Variance1.0856185
MonotonicityNot monotonic
2023-12-10T19:16:45.559376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.5430104936028 1
 
1.0%
37.4894811841473 1
 
1.0%
37.6238643287218 1
 
1.0%
35.253330774801 1
 
1.0%
35.8094772639744 1
 
1.0%
37.5427057429377 1
 
1.0%
35.1640753919658 1
 
1.0%
35.8659562862231 1
 
1.0%
37.7397859837443 1
 
1.0%
35.1548830715596 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.0583160272207 1
1.0%
34.6213028400871 1
1.0%
34.9122604245942 1
1.0%
35.0232664604366 1
1.0%
35.0879964261252 1
1.0%
35.115181522565 1
1.0%
35.1226897637083 1
1.0%
35.1285845645253 1
1.0%
35.1336815181856 1
1.0%
35.1548830715596 1
1.0%
ValueCountFrequency (%)
38.2009537824672 1
1.0%
37.900692774315 1
1.0%
37.8671629356178 1
1.0%
37.8242752070082 1
1.0%
37.7626666342184 1
1.0%
37.7453166178735 1
1.0%
37.7397859837443 1
1.0%
37.7342241856854 1
1.0%
37.7115763315234 1
1.0%
37.6749353089235 1
1.0%

grid_cd
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:45.929214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters16
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 row마마668290
2nd row다바340863
3rd row다사415477
4th row다사597427
5th row다사838167
ValueCountFrequency (%)
마마668290 1
 
1.0%
다사348439 1
 
1.0%
마라558966 1
 
1.0%
라마881574 1
 
1.0%
다사617493 1
 
1.0%
다라468855 1
 
1.0%
라마870636 1
 
1.0%
다사593712 1
 
1.0%
다라372846 1
 
1.0%
라사197852 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:16:46.507733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 83
10.4%
3 76
9.5%
6 73
9.1%
5 72
9.0%
다 63
 
7.9%
8 58
 
7.2%
7 57
 
7.1%
2 48
 
6.0%
1 48
 
6.0%
사 46
 
5.8%
Other values (6) 176
22.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 83
13.8%
3 76
12.7%
6 73
12.2%
5 72
12.0%
8 58
9.7%
7 57
9.5%
2 48
8.0%
1 48
8.0%
9 44
7.3%
0 41
6.8%
Other Letter
ValueCountFrequency (%)
다 63
31.5%
사 46
23.0%
마 45
22.5%
라 35
17.5%
바 10
 
5.0%
아 1
 
0.5%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
4 83
13.8%
3 76
12.7%
6 73
12.2%
5 72
12.0%
8 58
9.7%
7 57
9.5%
2 48
8.0%
1 48
8.0%
9 44
7.3%
0 41
6.8%
Hangul
ValueCountFrequency (%)
다 63
31.5%
사 46
23.0%
마 45
22.5%
라 35
17.5%
바 10
 
5.0%
아 1
 
0.5%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 83
13.8%
3 76
12.7%
6 73
12.2%
5 72
12.0%
8 58
9.7%
7 57
9.5%
2 48
8.0%
1 48
8.0%
9 44
7.3%
0 41
6.8%
Hangul
ValueCountFrequency (%)
다 63
31.5%
사 46
23.0%
마 45
22.5%
라 35
17.5%
바 10
 
5.0%
아 1
 
0.5%

lst_updt_dt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210916172801
100 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210916172801
2nd row20210916172801
3rd row20210916172801
4th row20210916172801
5th row20210916172801

Common Values

ValueCountFrequency (%)
20210916172801 100
100.0%

Length

2023-12-10T19:16:46.718961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:46.859263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210916172801 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

2023-12-10T19:16:47.000583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:47.145445image/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_498_DMSTC_MCST_PBL_CT_PARK_2021
100 

Length

Max length34
Median length34
Mean length34
Min length34

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_498_DMSTC_MCST_PBL_CT_PARK_2021
2nd rowKC_498_DMSTC_MCST_PBL_CT_PARK_2021
3rd rowKC_498_DMSTC_MCST_PBL_CT_PARK_2021
4th rowKC_498_DMSTC_MCST_PBL_CT_PARK_2021
5th rowKC_498_DMSTC_MCST_PBL_CT_PARK_2021

Common Values

ValueCountFrequency (%)
KC_498_DMSTC_MCST_PBL_CT_PARK_2021 100
100.0%

Length

2023-12-10T19:16:47.301924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:47.436955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_498_dmstc_mcst_pbl_ct_park_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

2023-12-10T19:16:47.572843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:16:47.695028image/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
0KCDPCPO21N000000001장소관광지213411근린공원<NA><NA>50705지역근린공원3114010600115530240울산광역시남구삼산동<NA>1553-131140106003114057000<NA><NA><NA>129.3411235.54301마마66829020210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
1KCDPCPO21N000008640장소관광지20511711공원아산국가산단<NA><NA>50705지역근린공원4427025337104039936충청남도당진시송악읍한진리40444270253374427025300442704595042고대공단1길10-6126.75874936.973107다바34086320210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
2KCDPCPO21N000000003장소관광지231585신월어린이공원<NA><NA>50705지역근린공원1147010300102400000서울특별시양천구신월동<NA>24011470103001147056000<NA><NA><NA>126.83812337.527001다사41547720210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
3KCDPCPO21N000000004장소관광지269048까치공원<NA><NA>50705지역근린공원1168011800104549888서울특별시강남구도곡동<NA>45511680118001168065600116804166169논현로26길34127.045237.483181다사59742720210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
4KCDPCPO21N000000005장소관광지540446수양관공원<NA><NA>50705지역근린공원4146136025102139904경기도용인시 처인구양지면추계리214-441461360254146136000<NA><NA><NA>127.31834737.249198다사83816720210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
5KCDPCPO21N000000006장소관광지539477관음공원<NA><NA>50705지역근린공원2723012500113719808대구광역시북구관음동<NA>137227230125002723075000272304235361구암로47128.5470435.935265라마94471420210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
6KCDPCPO21N000000007장소관광지539762강변체육공원<NA><NA>50705지역근린공원2723011800112339968대구광역시북구읍내동<NA>1234-15927230118002723077000<NA><NA><NA>128.55706135.940083라마95372020210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
7KCDPCPO21N000008641장소관광지20664022철쭉공원<NA><NA>50705지역근린공원4141010400111529984경기도군포시산본동<NA>115341410104004141059000414103200025번영로407126.92583537.353227다사49128320210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
8KCDPCPO21N000000009장소관광지540001작전공원<NA><NA>50705지역근린공원2824510300104139776인천광역시계양구작전동<NA>41428245103002824562100282454265279아나지로307번길1126.72574937.526219다사31547720210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
9KCDPCPO21N000000010장소관광지540220남산공원<NA><NA>50705지역근린공원4715010700100689920경상북도김천시황금동<NA>69-147150107004715053600471504718171남산공원길90-14128.11998336.117015라마55791220210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
idlclasmlsfcid_poipoi_nmbranch_nmsub_nmmcate_cdmcate_nmpnusido_nmsgg_nmbemd_nmri_nmbeonjibadm_cdhadm_cdrd_cdrd_nmbld_numxygrid_cdlst_updt_dtdata_orgnfile_namebase_ymd
90KCDPCPO21N000000091장소관광지17776974고전면생활체육공원<NA><NA>50705지역근린공원4885035021105829888경상남도하동군고전면범아리583-248850350214885035000<NA><NA><NA>127.81570635.023266라라28869820210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
91KCDPCPO21N000000092장소관광지17814209강진만생태공원<NA><NA>50705지역근린공원4681025025104369664전라남도강진군강진읍남포리43746810250274681025000468103293010남당로97-73126.77455734.621303다라33425420210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
92KCDPCPO21N000000093장소관광지17835752감천산림공원<NA><NA>50705지역근린공원2638010800201299968부산광역시사하구감천동<NA>산13026380108002638062000<NA><NA><NA>129.00889235.087996마라37578020210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
93KCDPCPO21N000000094장소관광지17847103다도해해상국립공원거문백도지구<NA>50701국립공원<NA>전라남도여수시삼산면동도리<NA>46130360244613036000<NA><NA><NA>127.47616734.058316다다97862820210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
94KCDPCPO21N000000095장소관광지18004909옥정중앙공원<NA><NA>50705지역근린공원4163011400110389760경기도양주시옥정동<NA>103941630114004163056000<NA><NA><NA>127.09684637.824275다사64580520210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
95KCDPCPO21N000000096장소관광지18048925덕천1어린이공원<NA><NA>50705지역근린공원4117110100113869824경기도안양시 만안구안양동<NA>138741171101004117157000<NA><NA><NA>126.9291837.395811다사49433120210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
96KCDPCPO21N000000097장소관광지18103646천사어린이공원<NA><NA>50705지역근린공원4427037022107200000충청남도당진시우강면송산리72044270370224427037000<NA><NA><NA>126.77495636.811859다바35368420210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
97KCDPCPO21N000000098장소관광지18237805형산강테마파크유림숲<NA><NA>50705지역근린공원4713012400110550016경상북도경주시황성동<NA>105547130124004713062100<NA><NA><NA>129.20944135.868847마마54364920210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
98KCDPCPO21N000000099장소관광지18266209등나무근린공원<NA><NA>50705지역근린공원1135010600105080064서울특별시노원구중계동<NA>50811350106001135062500113504130199동일로1238127.06709937.640106다사61860120210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916
99KCDPCPO21N000000100장소관광지18449065손흥민체육공원<NA><NA>50705지역근린공원4211031024201849856강원도춘천시동면감정리산18542110310244211031000421103013015가락재로40127.77844337.900693라사24489020210916172801KTKC_498_DMSTC_MCST_PBL_CT_PARK_202120210916