Overview

Dataset statistics

Number of variables13
Number of observations75
Missing cells104
Missing cells (%)10.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory110.8 B

Variable types

Categorical4
Text4
Numeric5

Dataset

Description수도권1호선의 철도운영기관명,선명,역명,환승주차장명,출구번호,주차면수,운영시간_평일,운영시간_휴일,주차요금,월정기권,기타요금,환승주차장운영기관,전화번호 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15086926/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
운영시간_휴일 is highly overall correlated with 운영시간_평일High correlation
운영시간_평일 is highly overall correlated with 운영시간_휴일High correlation
주차요금 is highly overall correlated with 월정기권 and 1 other fieldsHigh correlation
월정기권 is highly overall correlated with 주차요금 and 1 other fieldsHigh correlation
기타요금 is highly overall correlated with 주차요금 and 1 other fieldsHigh correlation
운영시간_평일 is highly imbalanced (54.4%)Imbalance
운영시간_휴일 is highly imbalanced (53.2%)Imbalance
출구번호 has 1 (1.3%) missing valuesMissing
주차면수 has 3 (4.0%) missing valuesMissing
주차요금 has 6 (8.0%) missing valuesMissing
월정기권 has 24 (32.0%) missing valuesMissing
기타요금 has 55 (73.3%) missing valuesMissing
환승주차장운영기관 has 3 (4.0%) missing valuesMissing
전화번호 has 12 (16.0%) missing valuesMissing
주차요금 has 9 (12.0%) zerosZeros
월정기권 has 5 (6.7%) zerosZeros
기타요금 has 2 (2.7%) zerosZeros

Reproduction

Analysis started2023-12-12 02:51:27.166645
Analysis finished2023-12-12 02:51:31.252563
Duration4.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
코레일
75 

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 75
100.0%

Length

2023-12-12T11:51:31.315368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:31.418408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코레일 75
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
1호선
75 

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 75
100.0%

Length

2023-12-12T11:51:31.520152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:31.623823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 75
100.0%

역명
Text

Distinct50
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-12T11:51:31.850739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.4133333
Min length2

Characters and Unicode

Total characters181
Distinct characters75
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

Unique31 ?
Unique (%)41.3%

Sample

1st row가능
2nd row가능
3rd row가능
4th row간석
5th row관악
ValueCountFrequency (%)
관악 4
 
5.3%
광명 4
 
5.3%
가능 3
 
4.0%
성균관대 3
 
4.0%
화서 2
 
2.7%
오산대 2
 
2.7%
수원 2
 
2.7%
양주 2
 
2.7%
오류동 2
 
2.7%
봉명 2
 
2.7%
Other values (40) 49
65.3%
2023-12-12T11:51:32.259603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 9
 
5.0%
관 7
 
3.9%
대 7
 
3.9%
명 7
 
3.9%
동 7
 
3.9%
오 6
 
3.3%
천 5
 
2.8%
정 5
 
2.8%
광 5
 
2.8%
악 4
 
2.2%
Other values (65) 119
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 179
98.9%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 9
 
5.0%
관 7
 
3.9%
대 7
 
3.9%
명 7
 
3.9%
동 7
 
3.9%
오 6
 
3.4%
천 5
 
2.8%
정 5
 
2.8%
광 5
 
2.8%
악 4
 
2.2%
Other values (63) 117
65.4%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 179
98.9%
Common 2
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 9
 
5.0%
관 7
 
3.9%
대 7
 
3.9%
명 7
 
3.9%
동 7
 
3.9%
오 6
 
3.4%
천 5
 
2.8%
정 5
 
2.8%
광 5
 
2.8%
악 4
 
2.2%
Other values (63) 117
65.4%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 179
98.9%
ASCII 2
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 9
 
5.0%
관 7
 
3.9%
대 7
 
3.9%
명 7
 
3.9%
동 7
 
3.9%
오 6
 
3.4%
천 5
 
2.8%
정 5
 
2.8%
광 5
 
2.8%
악 4
 
2.2%
Other values (63) 117
65.4%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

환승주차장명
Text

Distinct73
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-12T11:51:32.546199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length8.1866667
Min length3

Characters and Unicode

Total characters614
Distinct characters113
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)94.7%

Sample

1st row가능역A
2nd row가능역B
3rd row가능역C
4th row주안6동제2노외주차장
5th row관악역1환승
ValueCountFrequency (%)
주차장 15
 
12.1%
공영주차장 13
 
10.5%
환승주차장 3
 
2.4%
유료 3
 
2.4%
봉명역 2
 
1.6%
오산역 2
 
1.6%
병점역 2
 
1.6%
백운역 2
 
1.6%
노상 2
 
1.6%
금천교 2
 
1.6%
Other values (76) 78
62.9%
2023-12-12T11:51:32.990270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 69
 
11.2%
주 67
 
10.9%
차 64
 
10.4%
49
 
8.0%
역 46
 
7.5%
공 23
 
3.7%
영 21
 
3.4%
환 18
 
2.9%
승 17
 
2.8%
산 8
 
1.3%
Other values (103) 232
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 532
86.6%
Space Separator 49
 
8.0%
Uppercase Letter 17
 
2.8%
Decimal Number 10
 
1.6%
Open Punctuation 3
 
0.5%
Close Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 69
 
13.0%
주 67
 
12.6%
차 64
 
12.0%
역 46
 
8.6%
공 23
 
4.3%
영 21
 
3.9%
환 18
 
3.4%
승 17
 
3.2%
산 8
 
1.5%
동 7
 
1.3%
Other values (89) 192
36.1%
Uppercase Letter
ValueCountFrequency (%)
A 7
41.2%
B 5
29.4%
C 2
 
11.8%
J 1
 
5.9%
K 1
 
5.9%
D 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 4
40.0%
1 3
30.0%
6 1
 
10.0%
4 1
 
10.0%
3 1
 
10.0%
Space Separator
ValueCountFrequency (%)
49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 532
86.6%
Common 65
 
10.6%
Latin 17
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 69
 
13.0%
주 67
 
12.6%
차 64
 
12.0%
역 46
 
8.6%
공 23
 
4.3%
영 21
 
3.9%
환 18
 
3.4%
승 17
 
3.2%
산 8
 
1.5%
동 7
 
1.3%
Other values (89) 192
36.1%
Common
ValueCountFrequency (%)
49
75.4%
2 4
 
6.2%
( 3
 
4.6%
) 3
 
4.6%
1 3
 
4.6%
6 1
 
1.5%
4 1
 
1.5%
3 1
 
1.5%
Latin
ValueCountFrequency (%)
A 7
41.2%
B 5
29.4%
C 2
 
11.8%
J 1
 
5.9%
K 1
 
5.9%
D 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 532
86.6%
ASCII 82
 
13.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 69
 
13.0%
주 67
 
12.6%
차 64
 
12.0%
역 46
 
8.6%
공 23
 
4.3%
영 21
 
3.9%
환 18
 
3.4%
승 17
 
3.2%
산 8
 
1.5%
동 7
 
1.3%
Other values (89) 192
36.1%
ASCII
ValueCountFrequency (%)
49
59.8%
A 7
 
8.5%
B 5
 
6.1%
2 4
 
4.9%
( 3
 
3.7%
) 3
 
3.7%
1 3
 
3.7%
C 2
 
2.4%
J 1
 
1.2%
K 1
 
1.2%
Other values (4) 4
 
4.9%

출구번호
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)8.1%
Missing1
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean1.9864865
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T11:51:33.147120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32.75
95-th percentile4.35
Maximum7
Range6
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.2440901
Coefficient of variation (CV)0.62627663
Kurtosis3.0110709
Mean1.9864865
Median Absolute Deviation (MAD)1
Skewness1.6055177
Sum147
Variance1.5477601
MonotonicityNot monotonic
2023-12-12T11:51:33.268024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 34
45.3%
2 21
28.0%
3 11
 
14.7%
4 4
 
5.3%
5 3
 
4.0%
7 1
 
1.3%
(Missing) 1
 
1.3%
ValueCountFrequency (%)
1 34
45.3%
2 21
28.0%
3 11
 
14.7%
4 4
 
5.3%
5 3
 
4.0%
7 1
 
1.3%
ValueCountFrequency (%)
7 1
 
1.3%
5 3
 
4.0%
4 4
 
5.3%
3 11
 
14.7%
2 21
28.0%
1 34
45.3%

주차면수
Real number (ℝ)

MISSING 

Distinct57
Distinct (%)79.2%
Missing3
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean125.01389
Minimum10
Maximum1807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T11:51:33.428039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16.55
Q138
median65
Q3104.5
95-th percentile344.95
Maximum1807
Range1797
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation244.18969
Coefficient of variation (CV)1.9533005
Kurtosis34.241013
Mean125.01389
Median Absolute Deviation (MAD)31
Skewness5.4964156
Sum9001
Variance59628.605
MonotonicityNot monotonic
2023-12-12T11:51:33.613070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 3
 
4.0%
65 3
 
4.0%
50 3
 
4.0%
85 3
 
4.0%
76 2
 
2.7%
35 2
 
2.7%
10 2
 
2.7%
30 2
 
2.7%
22 2
 
2.7%
100 2
 
2.7%
Other values (47) 48
64.0%
(Missing) 3
 
4.0%
ValueCountFrequency (%)
10 2
2.7%
15 1
1.3%
16 1
1.3%
17 1
1.3%
19 1
1.3%
22 2
2.7%
23 1
1.3%
27 1
1.3%
29 1
1.3%
30 2
2.7%
ValueCountFrequency (%)
1807 1
1.3%
1019 1
1.3%
496 1
1.3%
362 1
1.3%
331 1
1.3%
290 1
1.3%
216 1
1.3%
198 1
1.3%
195 1
1.3%
172 1
1.3%

운영시간_평일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
00:00~23:59
56 
00:00~24:00
6 
09:00~18:00
 
4
09:00~19:00
 
3
08:00~17:00
 
1
Other values (5)
 
5

Length

Max length11
Median length11
Mean length10.906667
Min length4

Unique

Unique6 ?
Unique (%)8.0%

Sample

1st row00:00~23:59
2nd row00:00~23:59
3rd row00:00~23:59
4th row00:00~23:59
5th row00:00~23:59

Common Values

ValueCountFrequency (%)
00:00~23:59 56
74.7%
00:00~24:00 6
 
8.0%
09:00~18:00 4
 
5.3%
09:00~19:00 3
 
4.0%
08:00~17:00 1
 
1.3%
09:00~17:00 1
 
1.3%
04:30~00:30 1
 
1.3%
<NA> 1
 
1.3%
07:00~17:00 1
 
1.3%
14:00~22:00 1
 
1.3%

Length

2023-12-12T11:51:33.784390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:33.917786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00~23:59 56
74.7%
00:00~24:00 6
 
8.0%
09:00~18:00 4
 
5.3%
09:00~19:00 3
 
4.0%
08:00~17:00 1
 
1.3%
09:00~17:00 1
 
1.3%
04:30~00:30 1
 
1.3%
na 1
 
1.3%
07:00~17:00 1
 
1.3%
14:00~22:00 1
 
1.3%

운영시간_휴일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
00:00~23:59
57 
<NA>
9 
00:00~24:00
6 
09:00~18:00
 
1
04:30~00:30
 
1

Length

Max length11
Median length11
Mean length10.16
Min length4

Unique

Unique3 ?
Unique (%)4.0%

Sample

1st row00:00~23:59
2nd row00:00~23:59
3rd row00:00~23:59
4th row00:00~23:59
5th row00:00~23:59

Common Values

ValueCountFrequency (%)
00:00~23:59 57
76.0%
<NA> 9
 
12.0%
00:00~24:00 6
 
8.0%
09:00~18:00 1
 
1.3%
04:30~00:30 1
 
1.3%
07:00~17:00 1
 
1.3%

Length

2023-12-12T11:51:34.087980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:34.209384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00~23:59 57
76.0%
na 9
 
12.0%
00:00~24:00 6
 
8.0%
09:00~18:00 1
 
1.3%
04:30~00:30 1
 
1.3%
07:00~17:00 1
 
1.3%

주차요금
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)26.1%
Missing6
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean795.50725
Minimum0
Maximum3000
Zeros9
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T11:51:34.337080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median600
Q31000
95-th percentile2256
Maximum3000
Range3000
Interquartile range (IQR)700

Descriptive statistics

Standard deviation663.31063
Coefficient of variation (CV)0.83382097
Kurtosis1.3731374
Mean795.50725
Median Absolute Deviation (MAD)400
Skewness1.1658439
Sum54890
Variance439980.99
MonotonicityNot monotonic
2023-12-12T11:51:34.458071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1000 13
17.3%
0 9
12.0%
200 8
10.7%
500 7
9.3%
600 7
9.3%
1500 4
 
5.3%
800 3
 
4.0%
700 3
 
4.0%
2400 3
 
4.0%
1800 2
 
2.7%
Other values (8) 10
13.3%
(Missing) 6
8.0%
ValueCountFrequency (%)
0 9
12.0%
200 8
10.7%
300 2
 
2.7%
400 2
 
2.7%
500 7
9.3%
600 7
9.3%
700 3
 
4.0%
800 3
 
4.0%
900 1
 
1.3%
1000 13
17.3%
ValueCountFrequency (%)
3000 1
 
1.3%
2400 3
 
4.0%
2040 1
 
1.3%
1800 2
 
2.7%
1650 1
 
1.3%
1500 4
 
5.3%
1200 1
 
1.3%
1100 1
 
1.3%
1000 13
17.3%
900 1
 
1.3%

월정기권
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)35.3%
Missing24
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean57529.412
Minimum0
Maximum165000
Zeros5
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T11:51:34.580780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140500
median60000
Q370000
95-th percentile112000
Maximum165000
Range165000
Interquartile range (IQR)29500

Descriptive statistics

Standard deviation32802.654
Coefficient of variation (CV)0.57018928
Kurtosis1.6704367
Mean57529.412
Median Absolute Deviation (MAD)15000
Skewness0.61930324
Sum2934000
Variance1.0760141 × 109
MonotonicityNot monotonic
2023-12-12T11:51:34.714582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
70000 12
16.0%
60000 7
 
9.3%
45000 5
 
6.7%
0 5
 
6.7%
50000 5
 
6.7%
32000 3
 
4.0%
36000 2
 
2.7%
100000 2
 
2.7%
165000 1
 
1.3%
25000 1
 
1.3%
Other values (8) 8
 
10.7%
(Missing) 24
32.0%
ValueCountFrequency (%)
0 5
6.7%
20000 1
 
1.3%
25000 1
 
1.3%
30000 1
 
1.3%
32000 3
4.0%
36000 2
 
2.7%
45000 5
6.7%
50000 5
6.7%
60000 7
9.3%
66000 1
 
1.3%
ValueCountFrequency (%)
165000 1
 
1.3%
130000 1
 
1.3%
120000 1
 
1.3%
104000 1
 
1.3%
100000 2
 
2.7%
91000 1
 
1.3%
80000 1
 
1.3%
70000 12
16.0%
66000 1
 
1.3%
60000 7
9.3%

기타요금
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)35.0%
Missing55
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean318
Minimum0
Maximum800
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T11:51:34.828814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1200
median300
Q3525
95-th percentile610
Maximum800
Range800
Interquartile range (IQR)325

Descriptive statistics

Standard deviation225.98556
Coefficient of variation (CV)0.71064642
Kurtosis-0.54207599
Mean318
Median Absolute Deviation (MAD)150
Skewness0.54821291
Sum6360
Variance51069.474
MonotonicityNot monotonic
2023-12-12T11:51:34.970774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
200 5
 
6.7%
300 5
 
6.7%
600 4
 
5.3%
0 2
 
2.7%
80 2
 
2.7%
500 1
 
1.3%
800 1
 
1.3%
(Missing) 55
73.3%
ValueCountFrequency (%)
0 2
 
2.7%
80 2
 
2.7%
200 5
6.7%
300 5
6.7%
500 1
 
1.3%
600 4
5.3%
800 1
 
1.3%
ValueCountFrequency (%)
800 1
 
1.3%
600 4
5.3%
500 1
 
1.3%
300 5
6.7%
200 5
6.7%
80 2
 
2.7%
0 2
 
2.7%

환승주차장운영기관
Text

MISSING 

Distinct40
Distinct (%)55.6%
Missing3
Missing (%)4.0%
Memory size732.0 B
2023-12-12T11:51:35.211599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length6.8194444
Min length2

Characters and Unicode

Total characters491
Distinct characters87
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)34.7%

Sample

1st rowKN
2nd rowKN
3rd rowKN
4th row미추홀구시설관리공단
5th row경기도 안양도시공사
ValueCountFrequency (%)
코레일네트웍스 8
 
9.0%
경기도 7
 
7.9%
안양도시공사 5
 
5.6%
코레일 5
 
5.6%
수원도시공사 5
 
5.6%
시설관리공단 4
 
4.5%
kn 3
 
3.4%
서울시설공단 3
 
3.4%
네트웍스 3
 
3.4%
평택도시공사 3
 
3.4%
Other values (32) 43
48.3%
2023-12-12T11:51:35.607447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 42
 
8.6%
공 31
 
6.3%
도 28
 
5.7%
코 21
 
4.3%
일 21
 
4.3%
스 21
 
4.3%
레 21
 
4.3%
트 19
 
3.9%
네 18
 
3.7%
17
 
3.5%
Other values (77) 252
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 452
92.1%
Space Separator 17
 
3.5%
Uppercase Letter 12
 
2.4%
Other Symbol 5
 
1.0%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 42
 
9.3%
공 31
 
6.9%
도 28
 
6.2%
코 21
 
4.6%
일 21
 
4.6%
스 21
 
4.6%
레 21
 
4.6%
트 19
 
4.2%
네 18
 
4.0%
사 17
 
3.8%
Other values (66) 213
47.1%
Uppercase Letter
ValueCountFrequency (%)
K 4
33.3%
N 3
25.0%
A 2
16.7%
J 1
 
8.3%
G 1
 
8.3%
M 1
 
8.3%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 457
93.1%
Common 22
 
4.5%
Latin 12
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 42
 
9.2%
공 31
 
6.8%
도 28
 
6.1%
코 21
 
4.6%
일 21
 
4.6%
스 21
 
4.6%
레 21
 
4.6%
트 19
 
4.2%
네 18
 
3.9%
사 17
 
3.7%
Other values (67) 218
47.7%
Latin
ValueCountFrequency (%)
K 4
33.3%
N 3
25.0%
A 2
16.7%
J 1
 
8.3%
G 1
 
8.3%
M 1
 
8.3%
Common
ValueCountFrequency (%)
17
77.3%
) 2
 
9.1%
( 2
 
9.1%
& 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 452
92.1%
ASCII 34
 
6.9%
None 5
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 42
 
9.3%
공 31
 
6.9%
도 28
 
6.2%
코 21
 
4.6%
일 21
 
4.6%
스 21
 
4.6%
레 21
 
4.6%
트 19
 
4.2%
네 18
 
4.0%
사 17
 
3.8%
Other values (66) 213
47.1%
ASCII
ValueCountFrequency (%)
17
50.0%
K 4
 
11.8%
N 3
 
8.8%
A 2
 
5.9%
) 2
 
5.9%
( 2
 
5.9%
J 1
 
2.9%
G 1
 
2.9%
& 1
 
2.9%
M 1
 
2.9%
None
ValueCountFrequency (%)
㈜ 5
100.0%

전화번호
Text

MISSING 

Distinct39
Distinct (%)61.9%
Missing12
Missing (%)16.0%
Memory size732.0 B
2023-12-12T11:51:35.819573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.698413
Min length9

Characters and Unicode

Total characters737
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

Unique33 ?
Unique (%)52.4%

Sample

1st row02-3271-0720
2nd row02-3271-0720
3rd row02-3271-0720
4th row032-225-0271
5th row031-389-5323
ValueCountFrequency (%)
02-3271-0720 16
25.4%
031-389-5323 4
 
6.3%
1588-7270 3
 
4.8%
031-692-3431 3
 
4.8%
031-371-1830 2
 
3.2%
02-3405-4599 2
 
3.2%
031-461-8831 1
 
1.6%
031-389-5326 1
 
1.6%
031-278-1491 1
 
1.6%
031-258-2013 1
 
1.6%
Other values (29) 29
46.0%
2023-12-12T11:51:36.157456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 125
17.0%
- 120
16.3%
2 92
12.5%
3 90
12.2%
1 84
11.4%
7 76
10.3%
8 50
 
6.8%
5 29
 
3.9%
9 28
 
3.8%
6 25
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 617
83.7%
Dash Punctuation 120
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125
20.3%
2 92
14.9%
3 90
14.6%
1 84
13.6%
7 76
12.3%
8 50
 
8.1%
5 29
 
4.7%
9 28
 
4.5%
6 25
 
4.1%
4 18
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 737
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125
17.0%
- 120
16.3%
2 92
12.5%
3 90
12.2%
1 84
11.4%
7 76
10.3%
8 50
 
6.8%
5 29
 
3.9%
9 28
 
3.8%
6 25
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125
17.0%
- 120
16.3%
2 92
12.5%
3 90
12.2%
1 84
11.4%
7 76
10.3%
8 50
 
6.8%
5 29
 
3.9%
9 28
 
3.8%
6 25
 
3.4%

Interactions

2023-12-12T11:51:30.320110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:27.833193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.429144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.983305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:29.521087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.421432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:27.945421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.549366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:29.107828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:29.619431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.505776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.088482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.650344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:29.208240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.007683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.585924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.211119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.753768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:29.313298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.116355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.666064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.332418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:28.871396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:29.425484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:30.220199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:51:36.261336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명환승주차장명출구번호주차면수운영시간_평일운영시간_휴일주차요금월정기권기타요금환승주차장운영기관전화번호
역명1.0001.0000.5840.0000.8100.0000.9150.9570.9080.9930.952
환승주차장명1.0001.0000.9681.0001.0001.0001.0001.0001.0001.0001.000
출구번호0.5840.9681.0000.4040.1970.0000.3550.5160.0000.6440.938
주차면수0.0001.0000.4041.0000.0000.0000.6280.4010.6190.0001.000
운영시간_평일0.8101.0000.1970.0001.0001.0000.0000.0000.0000.8200.822
운영시간_휴일0.0001.0000.0000.0001.0001.0000.0000.0000.0000.0000.815
주차요금0.9151.0000.3550.6280.0000.0001.0000.7920.9500.8190.886
월정기권0.9571.0000.5160.4010.0000.0000.7921.0000.8850.9400.876
기타요금0.9081.0000.0000.6190.0000.0000.9500.8851.0000.8510.963
환승주차장운영기관0.9931.0000.6440.0000.8200.0000.8190.9400.8511.0000.971
전화번호0.9521.0000.9381.0000.8220.8150.8860.8760.9630.9711.000
2023-12-12T11:51:36.379124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영시간_휴일운영시간_평일
운영시간_휴일1.0000.992
운영시간_평일0.9921.000
2023-12-12T11:51:36.453355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출구번호주차면수주차요금월정기권기타요금운영시간_평일운영시간_휴일
출구번호1.0000.1570.126-0.0980.1410.0880.000
주차면수0.1571.0000.173-0.1560.2690.0000.000
주차요금0.1260.1731.0000.6780.7700.0000.000
월정기권-0.098-0.1560.6781.0000.8550.0000.000
기타요금0.1410.2690.7700.8551.0000.0000.000
운영시간_평일0.0880.0000.0000.0000.0001.0000.992
운영시간_휴일0.0000.0000.0000.0000.0000.9921.000

Missing values

2023-12-12T11:51:30.799974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:51:30.966538image/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.
2023-12-12T11:51:31.148520image/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

철도운영기관명선명역명환승주차장명출구번호주차면수운영시간_평일운영시간_휴일주차요금월정기권기타요금환승주차장운영기관전화번호
0코레일1호선가능가능역A19000:00~23:5900:00~23:5980070000<NA>KN02-3271-0720
1코레일1호선가능가능역B33100:00~23:5900:00~23:5980070000<NA>KN02-3271-0720
2코레일1호선가능가능역C13300:00~23:5900:00~23:59<NA>70000<NA>KN02-3271-0720
3코레일1호선간석주안6동제2노외주차장13000:00~23:5900:00~23:5960060000<NA>미추홀구시설관리공단032-225-0271
4코레일1호선관악관악역1환승14400:00~23:5900:00~23:59<NA>45000<NA>경기도 안양도시공사031-389-5323
5코레일1호선관악관악역2환승14600:00~23:5900:00~23:5920045000<NA>경기도 안양도시공사031-389-5323
6코레일1호선관악관악역3환승29600:00~23:5900:00~23:5920045000<NA>경기도 안양도시공사031-389-5323
7코레일1호선관악관악역4환승211800:00~23:5900:00~23:5920045000<NA>경기도 안양도시공사031-389-5323
8코레일1호선광명C주차장533100:00~23:5900:00~23:592400<NA>600AJ파크1899-1485
9코레일1호선광명D주차장449600:00~23:5900:00~23:592400<NA>600나이스파크02-897-9615
철도운영기관명선명역명환승주차장명출구번호주차면수운영시간_평일운영시간_휴일주차요금월정기권기타요금환승주차장운영기관전화번호
65코레일1호선지행지행역 주차장312700:00~23:5900:00~23:5900<NA>경기도 동두천시청031-860-2751
66코레일1호선직산직산역 주차장18509:00~18:00<NA>50060000<NA>코레일 네트웍스02-3271-0720
67코레일1호선진위진위역환승주차장16000:00~23:5900:00~23:59100050000<NA>㈜코레일네트웍스02-3271-0720
68코레일1호선천안동부주차장15000:00~23:5900:00~23:59100080000<NA>코레일 네트웍스02-3271-0712
69코레일1호선평택원평제1공영주차장217200:00~23:5900:00~23:5950070000300평택도시공사031-692-3431
70코레일1호선평택원평제2공영주차장26500:00~23:5900:00~23:59500<NA>300평택도시공사031-692-3431
71코레일1호선화서화서환승공영주차장129000:00~23:5900:00~23:5950036000<NA>수원도시공사031-258-2013
72코레일1호선화서꽃뫼환승주차장514314:00~22:00<NA>30020000<NA>수원도시공사031-278-1491
73코레일1호선회룡회룡역 공영주차장319800:00~23:5900:00~23:59180070000<NA>의정부시청031-876-6311
74코레일1호선탕정탕정역 주차장15400:00~23:5900:00~23:590<NA><NA>코레일041-548-6788