Overview

Dataset statistics

Number of variables5
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory45.3 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description부산지하철1호선에서 관리하는 도시광역철도역들의 철도운영기관명, 선명, 역명, 경도, 위도의 데이터에 관한 파일데이터 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041165/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
경도 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 경도High correlation
역명 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:39:28.995777
Analysis finished2023-12-12 03:39:29.896691
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
부산교통공사
40 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산교통공사 40
100.0%

Length

2023-12-12T12:39:29.984891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:39:30.124410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산교통공사 40
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
1호선
40 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선
2nd row1호선
3rd row1호선
4th row1호선
5th row1호선

Common Values

ValueCountFrequency (%)
1호선 40
100.0%

Length

2023-12-12T12:39:30.259120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:39:30.376760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 40
100.0%

역명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T12:39:30.647024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length4.025
Min length2

Characters and Unicode

Total characters161
Distinct characters86
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row동매
2nd row신평
3rd row하단(부산본병원)
4th row당리(사하구청)
5th row사하
ValueCountFrequency (%)
동매 1
 
2.5%
신평 1
 
2.5%
장전(부산가톨릭대학교 1
 
2.5%
시청(연제 1
 
2.5%
연산 1
 
2.5%
교대 1
 
2.5%
동래 1
 
2.5%
명륜 1
 
2.5%
온천장 1
 
2.5%
부산대 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T12:39:31.117639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 10
 
6.2%
산 9
 
5.6%
) 8
 
5.0%
( 8
 
5.0%
부 8
 
5.0%
장 6
 
3.7%
동 4
 
2.5%
신 4
 
2.5%
포 4
 
2.5%
교 3
 
1.9%
Other values (76) 97
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
89.4%
Close Punctuation 8
 
5.0%
Open Punctuation 8
 
5.0%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 10
 
6.9%
산 9
 
6.2%
부 8
 
5.6%
장 6
 
4.2%
동 4
 
2.8%
신 4
 
2.8%
포 4
 
2.8%
교 3
 
2.1%
범 3
 
2.1%
학 3
 
2.1%
Other values (73) 90
62.5%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
89.4%
Common 17
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 10
 
6.9%
산 9
 
6.2%
부 8
 
5.6%
장 6
 
4.2%
동 4
 
2.8%
신 4
 
2.8%
포 4
 
2.8%
교 3
 
2.1%
범 3
 
2.1%
학 3
 
2.1%
Other values (73) 90
62.5%
Common
ValueCountFrequency (%)
) 8
47.1%
( 8
47.1%
· 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
89.4%
ASCII 16
 
9.9%
None 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 10
 
6.9%
산 9
 
6.2%
부 8
 
5.6%
장 6
 
4.2%
동 4
 
2.8%
신 4
 
2.8%
포 4
 
2.8%
교 3
 
2.1%
범 3
 
2.1%
학 3
 
2.1%
Other values (73) 90
62.5%
ASCII
ValueCountFrequency (%)
) 8
50.0%
( 8
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.03722
Minimum128.96056
Maximum129.09497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:39:31.304668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.96056
5-th percentile128.96667
Q1128.98988
median129.04547
Q3129.07975
95-th percentile129.09251
Maximum129.09497
Range0.134403
Interquartile range (IQR)0.08987

Descriptive statistics

Standard deviation0.045976794
Coefficient of variation (CV)0.00035630646
Kurtosis-1.3988913
Mean129.03722
Median Absolute Deviation (MAD)0.038519
Skewness-0.33889789
Sum5161.4887
Variance0.0021138655
MonotonicityNot monotonic
2023-12-12T12:39:31.469921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
128.9742 1
 
2.5%
129.071366 1
 
2.5%
129.081534 1
 
2.5%
129.080035 1
 
2.5%
129.078506 1
 
2.5%
129.079659 1
 
2.5%
129.086437 1
 
2.5%
129.089358 1
 
2.5%
129.088111 1
 
2.5%
129.091327 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
128.960564 1
2.5%
128.9641 1
2.5%
128.966803 1
2.5%
128.9713 1
2.5%
128.973846 1
2.5%
128.9742 1
2.5%
128.977041 1
2.5%
128.9775 1
2.5%
128.979873 1
2.5%
128.9831 1
2.5%
ValueCountFrequency (%)
129.094967 1
2.5%
129.092679 1
2.5%
129.092496 1
2.5%
129.091386 1
2.5%
129.091327 1
2.5%
129.089358 1
2.5%
129.088111 1
2.5%
129.086437 1
2.5%
129.081534 1
2.5%
129.080035 1
2.5%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.148069
Minimum35.04867
Maximum35.284687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:39:31.633000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.04867
5-th percentile35.064893
Q135.099839
median35.124477
Q335.198448
95-th percentile35.265789
Maximum35.284687
Range0.23601657
Interquartile range (IQR)0.0986085

Descriptive statistics

Standard deviation0.065797027
Coefficient of variation (CV)0.0018719955
Kurtosis-0.81389663
Mean35.148069
Median Absolute Deviation (MAD)0.0363435
Skewness0.59596461
Sum1405.9228
Variance0.0043292487
MonotonicityNot monotonic
2023-12-12T12:39:31.853071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
35.0899 1
 
2.5%
35.173122 1
 
2.5%
35.186168 1
 
2.5%
35.19605 1
 
2.5%
35.205641 1
 
2.5%
35.212551 1
 
2.5%
35.220249 1
 
2.5%
35.229609 1
 
2.5%
35.23809095 1
 
2.5%
35.247407 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
35.04867 1
2.5%
35.05782 1
2.5%
35.065265 1
2.5%
35.074433 1
2.5%
35.08109 1
2.5%
35.0899 1
2.5%
35.095179 1
2.5%
35.097372 1
2.5%
35.097953 1
2.5%
35.099816 1
2.5%
ValueCountFrequency (%)
35.28468657 1
2.5%
35.273105 1
2.5%
35.265404 1
2.5%
35.256959 1
2.5%
35.247407 1
2.5%
35.23809095 1
2.5%
35.229609 1
2.5%
35.220249 1
2.5%
35.212551 1
2.5%
35.205641 1
2.5%

Interactions

2023-12-12T12:39:29.422608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.208592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.595139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.312635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:39:31.960886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명경도위도
역명1.0001.0001.000
경도1.0001.0000.926
위도1.0000.9261.000
2023-12-12T12:39:32.067118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도
경도1.0000.948
위도0.9481.000

Missing values

2023-12-12T12:39:29.735809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:39:29.847575image/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

철도운영기관명선명역명경도위도
0부산교통공사1호선동매128.974235.0899
1부산교통공사1호선신평128.96056435.095179
2부산교통공사1호선하단(부산본병원)128.96680335.10618
3부산교통공사1호선당리(사하구청)128.97384635.103532
4부산교통공사1호선사하128.983135.099847
5부산교통공사1호선괴정128.99214435.099816
6부산교통공사1호선대티(동주대학)128.99993635.103126
7부산교통공사1호선서대신129.01217835.110937
8부산교통공사1호선동대신129.01768435.110452
9부산교통공사1호선토성129.01977635.100727
철도운영기관명선명역명경도위도
30부산교통공사1호선구서129.09132735.247407
31부산교통공사1호선두실129.09138635.256959
32부산교통공사1호선남산(부산외국대학교)129.09249635.265404
33부산교통공사1호선범어사129.09267935.273105
34부산교통공사1호선노포(종합버스터미널)129.09496735.284687
35부산교통공사1호선다대포해수욕장128.964135.04867
36부산교통공사1호선다대포항128.971335.05782
37부산교통공사1호선낫개128.97987335.065265
38부산교통공사1호선신장림128.97704135.074433
39부산교통공사1호선장림128.977535.08109