Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells6
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory47.3 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description경춘선에서 운영하는 도시광역철도역들의 철도운영기관명, 선명, 역명, 경도, 위도 에 대한 데이터가 있는 파일데이터 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041483/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
경도 has 3 (12.0%) missing valuesMissing
위도 has 3 (12.0%) missing valuesMissing
역명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:28:46.320436
Analysis finished2023-12-12 15:28:47.163565
Duration0.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
코레일
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
코레일 25
100.0%

Length

2023-12-13T00:28:47.237292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:28:47.358296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코레일 25
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
경춘
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경춘 25
100.0%

Length

2023-12-13T00:28:47.479903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:28:47.578895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경춘 25
100.0%

역명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T00:28:47.749477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.32
Min length2

Characters and Unicode

Total characters108
Distinct characters65
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

Unique25 ?
Unique (%)100.0%

Sample

1st row광운대
2nd row대성리
3rd row청량리
4th row회기
5th row중랑
ValueCountFrequency (%)
광운대 1
 
4.0%
평내호평 1
 
4.0%
춘천(한림대 1
 
4.0%
김유정 1
 
4.0%
강촌 1
 
4.0%
백양리(엘리시안강촌 1
 
4.0%
굴봉산(제이드가든 1
 
4.0%
가평(자라섬·남이섬 1
 
4.0%
상천(호명호수 1
 
4.0%
청평 1
 
4.0%
Other values (15) 15
60.0%
2023-12-13T00:28:48.066492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 7
 
6.5%
) 7
 
6.5%
대 5
 
4.6%
리 4
 
3.7%
천 4
 
3.7%
평 4
 
3.7%
강 3
 
2.8%
호 3
 
2.8%
내 3
 
2.8%
가 2
 
1.9%
Other values (55) 66
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
86.1%
Open Punctuation 7
 
6.5%
Close Punctuation 7
 
6.5%
Other Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 5
 
5.4%
리 4
 
4.3%
천 4
 
4.3%
평 4
 
4.3%
강 3
 
3.2%
호 3
 
3.2%
내 3
 
3.2%
가 2
 
2.2%
남 2
 
2.2%
원 2
 
2.2%
Other values (52) 61
65.6%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
86.1%
Common 15
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 5
 
5.4%
리 4
 
4.3%
천 4
 
4.3%
평 4
 
4.3%
강 3
 
3.2%
호 3
 
3.2%
내 3
 
3.2%
가 2
 
2.2%
남 2
 
2.2%
원 2
 
2.2%
Other values (52) 61
65.6%
Common
ValueCountFrequency (%)
( 7
46.7%
) 7
46.7%
· 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
86.1%
ASCII 14
 
13.0%
None 1
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 7
50.0%
) 7
50.0%
Hangul
ValueCountFrequency (%)
대 5
 
5.4%
리 4
 
4.3%
천 4
 
4.3%
평 4
 
4.3%
강 3
 
3.2%
호 3
 
3.2%
내 3
 
3.2%
가 2
 
2.2%
남 2
 
2.2%
원 2
 
2.2%
Other values (52) 61
65.6%
None
ValueCountFrequency (%)
· 1
100.0%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing3
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean127.34304
Minimum127.04504
Maximum127.72376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:28:48.180503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.04504
5-th percentile127.0589
Q1127.12721
median127.29872
Q3127.54597
95-th percentile127.71649
Maximum127.72376
Range0.678718
Interquartile range (IQR)0.41876425

Descriptive statistics

Standard deviation0.23709538
Coefficient of variation (CV)0.0018618638
Kurtosis-1.3626562
Mean127.34304
Median Absolute Deviation (MAD)0.1920835
Skewness0.34551471
Sum2801.5468
Variance0.056214221
MonotonicityNot monotonic
2023-12-13T00:28:48.312992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
127.127208 2
 
8.0%
127.426541 1
 
4.0%
127.723761 1
 
4.0%
127.716603 1
 
4.0%
127.71426 1
 
4.0%
127.634054 1
 
4.0%
127.589085 1
 
4.0%
127.557732 1
 
4.0%
127.510693 1
 
4.0%
127.4542 1
 
4.0%
Other values (11) 11
44.0%
(Missing) 3
 
12.0%
ValueCountFrequency (%)
127.045043 1
4.0%
127.057989 1
4.0%
127.076152 1
4.0%
127.103277 1
4.0%
127.11 1
4.0%
127.127208 2
8.0%
127.143858 1
4.0%
127.207931 1
4.0%
127.244576 1
4.0%
127.285731 1
4.0%
ValueCountFrequency (%)
127.723761 1
4.0%
127.716603 1
4.0%
127.71426 1
4.0%
127.634054 1
4.0%
127.589085 1
4.0%
127.557732 1
4.0%
127.510693 1
4.0%
127.4542 1
4.0%
127.426541 1
4.0%
127.379165 1
4.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing3
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean37.708294
Minimum37.580113
Maximum37.884391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:28:48.427031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.580113
5-th percentile37.590032
Q137.638616
median37.656202
Q337.812318
95-th percentile37.862472
Maximum37.884391
Range0.304278
Interquartile range (IQR)0.1737025

Descriptive statistics

Standard deviation0.099573799
Coefficient of variation (CV)0.0026406339
Kurtosis-1.3882736
Mean37.708294
Median Absolute Deviation (MAD)0.0638435
Skewness0.45088823
Sum829.58247
Variance0.0099149414
MonotonicityNot monotonic
2023-12-13T00:28:48.527261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
37.642221 2
 
8.0%
37.735484 1
 
4.0%
37.864071 1
 
4.0%
37.884391 1
 
4.0%
37.81841 1
 
4.0%
37.805728 1
 
4.0%
37.830881 1
 
4.0%
37.832086 1
 
4.0%
37.814515 1
 
4.0%
37.770405 1
 
4.0%
Other values (11) 11
44.0%
(Missing) 3
 
12.0%
ValueCountFrequency (%)
37.580113 1
4.0%
37.589774 1
4.0%
37.594944 1
4.0%
37.612855 1
4.0%
37.63 1
4.0%
37.637414 1
4.0%
37.642221 2
8.0%
37.648316 1
4.0%
37.652328 1
4.0%
37.65341 1
4.0%
ValueCountFrequency (%)
37.884391 1
4.0%
37.864071 1
4.0%
37.832086 1
4.0%
37.830881 1
4.0%
37.81841 1
4.0%
37.814515 1
4.0%
37.805728 1
4.0%
37.770405 1
4.0%
37.735484 1
4.0%
37.683909 1
4.0%

Interactions

2023-12-13T00:28:46.661366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:46.465747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:46.764213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:46.564348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:28:48.599869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명경도위도
역명1.0001.0001.000
경도1.0001.0000.879
위도1.0000.8791.000
2023-12-13T00:28:48.679259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도
경도1.0000.975
위도0.9751.000

Missing values

2023-12-13T00:28:46.919881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:28:47.014796image/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.
2023-12-13T00:28:47.110371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

철도운영기관명선명역명경도위도
0코레일경춘광운대<NA><NA>
1코레일경춘대성리127.37916537.683909
2코레일경춘청량리127.04504337.580113
3코레일경춘회기127.05798937.589774
4코레일경춘중랑127.07615237.594944
5코레일경춘상봉<NA><NA>
6코레일경춘망우<NA><NA>
7코레일경춘신내127.10327737.612855
8코레일경춘갈매127.1137.63
9코레일경춘별내(삼육대학교)127.12720837.642221
철도운영기관명선명역명경도위도
15코레일경춘마석127.31171337.652328
16코레일경춘청평127.42654137.735484
17코레일경춘상천(호명호수)127.454237.770405
18코레일경춘가평(자라섬·남이섬)127.51069337.814515
19코레일경춘굴봉산(제이드가든)127.55773237.832086
20코레일경춘백양리(엘리시안강촌)127.58908537.830881
21코레일경춘강촌127.63405437.805728
22코레일경춘김유정127.7142637.81841
23코레일경춘춘천(한림대)127.71660337.884391
24코레일경춘남춘천(강원대)127.72376137.864071