Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory110.6 B

Variable types

Text3
Numeric5
Categorical4

Dataset

Description대전도시철도 1호선 공구별, 위치,연장,시설물 형태,환기구연장,정거장 형식, 터널의형태등 토목시설물 정보를 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15043919/fileData.do

Alerts

합계(미터) is highly overall correlated with 유타입(미터) and 1 other fieldsHigh correlation
복선박스(미터) is highly overall correlated with 환기구(미터) and 2 other fieldsHigh correlation
환기구(미터) is highly overall correlated with 복선박스(미터) and 1 other fieldsHigh correlation
복선정거장(미터) is highly overall correlated with 환기구(미터)High correlation
단선터널(미터) is highly overall correlated with 복선박스(미터) and 2 other fieldsHigh correlation
유타입(미터) is highly overall correlated with 합계(미터) and 1 other fieldsHigh correlation
단선박스(미터) is highly overall correlated with 단선터널(미터) and 1 other fieldsHigh correlation
단선정거장(미터) is highly overall correlated with 합계(미터) and 1 other fieldsHigh correlation
복선터널(미터) is highly overall correlated with 단선박스(미터)High correlation
유타입(미터) is highly imbalanced (67.6%)Imbalance
단선박스(미터) is highly imbalanced (67.6%)Imbalance
단선정거장(미터) is highly imbalanced (74.2%)Imbalance
복선터널(미터) is highly imbalanced (56.3%)Imbalance
공구 has unique valuesUnique
위치 has unique valuesUnique
복선박스(미터) has 1 (4.3%) zerosZeros
환기구(미터) has 4 (17.4%) zerosZeros
복선정거장(미터) has 4 (17.4%) zerosZeros
단선터널(미터) has 18 (78.3%) zerosZeros

Reproduction

Analysis started2024-04-21 08:37:38.539727
Analysis finished2024-04-21 08:37:44.314307
Duration5.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공구
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-04-21T17:37:44.837512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6521739
Min length3

Characters and Unicode

Total characters84
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

Unique23 ?
Unique (%)100.0%

Sample

1st row1공구
2nd row위탁공구
3rd row2공구
4th row3공구
5th row4공구
ValueCountFrequency (%)
1공구 1
 
4.3%
12공구 1
 
4.3%
20공구 1
 
4.3%
반석천공구 1
 
4.3%
19공구 1
 
4.3%
18공구 1
 
4.3%
17공구 1
 
4.3%
16공구 1
 
4.3%
15공구 1
 
4.3%
14공구 1
 
4.3%
Other values (13) 13
56.5%
2024-04-21T17:37:45.709364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 23
27.4%
공 23
27.4%
1 13
15.5%
2 4
 
4.8%
6 2
 
2.4%
0 2
 
2.4%
9 2
 
2.4%
8 2
 
2.4%
7 2
 
2.4%
5 2
 
2.4%
Other values (7) 9
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
60.7%
Decimal Number 33
39.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
39.4%
2 4
 
12.1%
6 2
 
6.1%
0 2
 
6.1%
9 2
 
6.1%
8 2
 
6.1%
7 2
 
6.1%
5 2
 
6.1%
4 2
 
6.1%
3 2
 
6.1%
Other Letter
ValueCountFrequency (%)
구 23
45.1%
공 23
45.1%
탁 1
 
2.0%
위 1
 
2.0%
반 1
 
2.0%
석 1
 
2.0%
천 1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
60.7%
Common 33
39.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13
39.4%
2 4
 
12.1%
6 2
 
6.1%
0 2
 
6.1%
9 2
 
6.1%
8 2
 
6.1%
7 2
 
6.1%
5 2
 
6.1%
4 2
 
6.1%
3 2
 
6.1%
Hangul
ValueCountFrequency (%)
구 23
45.1%
공 23
45.1%
탁 1
 
2.0%
위 1
 
2.0%
반 1
 
2.0%
석 1
 
2.0%
천 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
60.7%
ASCII 33
39.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 23
45.1%
공 23
45.1%
탁 1
 
2.0%
위 1
 
2.0%
반 1
 
2.0%
석 1
 
2.0%
천 1
 
2.0%
ASCII
ValueCountFrequency (%)
1 13
39.4%
2 4
 
12.1%
6 2
 
6.1%
0 2
 
6.1%
9 2
 
6.1%
8 2
 
6.1%
7 2
 
6.1%
5 2
 
6.1%
4 2
 
6.1%
3 2
 
6.1%

위치
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-04-21T17:37:46.371838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.73913
Min length18

Characters and Unicode

Total characters454
Distinct characters57
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row기지∼판암(0k140∼0k730)
2nd row기지∼판암(0k730∼0k885)
3rd row판암∼신흥(0k885∼2k180)
4th row신흥∼대동(2k180∼3k560)
5th row대동∼대전(3k560∼4k723)
ValueCountFrequency (%)
기지∼판암(0k140∼0k730 1
 
4.3%
갈마∼월평(12k950∼14k130 1
 
4.3%
지족∼반석(21k706∼22k480 1
 
4.3%
지족∼반석(21k621∼21k706 1
 
4.3%
노은∼지족(20k520∼21k621 1
 
4.3%
월드컵∼노은(19k380∼20k520 1
 
4.3%
현충원∼월드컵(18k400∼19k380 1
 
4.3%
구암∼현충원(17k440∼18k400 1
 
4.3%
유성온천∼구암(16k460∼17k440 1
 
4.3%
갑천∼유성온천(15k220∼16k460 1
 
4.3%
Other values (13) 13
56.5%
2024-04-21T17:37:47.295007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 55
 
12.1%
∼ 46
 
10.1%
k 46
 
10.1%
1 31
 
6.8%
2 28
 
6.2%
( 23
 
5.1%
) 23
 
5.1%
4 21
 
4.6%
6 19
 
4.2%
8 12
 
2.6%
Other values (47) 150
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 211
46.5%
Other Letter 105
23.1%
Math Symbol 46
 
10.1%
Lowercase Letter 46
 
10.1%
Open Punctuation 23
 
5.1%
Close Punctuation 23
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 6
 
5.7%
청 5
 
4.8%
암 5
 
4.8%
대 5
 
4.8%
구 4
 
3.8%
천 4
 
3.8%
월 4
 
3.8%
중 3
 
2.9%
기 3
 
2.9%
전 3
 
2.9%
Other values (33) 63
60.0%
Decimal Number
ValueCountFrequency (%)
0 55
26.1%
1 31
14.7%
2 28
13.3%
4 21
 
10.0%
6 19
 
9.0%
8 12
 
5.7%
7 12
 
5.7%
5 12
 
5.7%
3 11
 
5.2%
9 10
 
4.7%
Math Symbol
ValueCountFrequency (%)
∼ 46
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 303
66.7%
Hangul 105
 
23.1%
Latin 46
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 6
 
5.7%
청 5
 
4.8%
암 5
 
4.8%
대 5
 
4.8%
구 4
 
3.8%
천 4
 
3.8%
월 4
 
3.8%
중 3
 
2.9%
기 3
 
2.9%
전 3
 
2.9%
Other values (33) 63
60.0%
Common
ValueCountFrequency (%)
0 55
18.2%
∼ 46
15.2%
1 31
10.2%
2 28
9.2%
( 23
7.6%
) 23
7.6%
4 21
 
6.9%
6 19
 
6.3%
8 12
 
4.0%
7 12
 
4.0%
Other values (3) 33
10.9%
Latin
ValueCountFrequency (%)
k 46
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 303
66.7%
Hangul 105
 
23.1%
Math Operators 46
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55
18.2%
k 46
15.2%
1 31
10.2%
2 28
9.2%
( 23
7.6%
) 23
7.6%
4 21
 
6.9%
6 19
 
6.3%
8 12
 
4.0%
7 12
 
4.0%
Other values (3) 33
10.9%
Math Operators
ValueCountFrequency (%)
∼ 46
100.0%
Hangul
ValueCountFrequency (%)
지 6
 
5.7%
청 5
 
4.8%
암 5
 
4.8%
대 5
 
4.8%
구 4
 
3.8%
천 4
 
3.8%
월 4
 
3.8%
중 3
 
2.9%
기 3
 
2.9%
전 3
 
2.9%
Other values (33) 63
60.0%

합계(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011.2174
Minimum85
Maximum1695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-04-21T17:37:47.500343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile163.2
Q1870
median1140
Q31229
95-th percentile1484.8
Maximum1695
Range1610
Interquartile range (IQR)359

Descriptive statistics

Standard deviation415.07031
Coefficient of variation (CV)0.41046595
Kurtosis0.56444281
Mean1011.2174
Median Absolute Deviation (MAD)170
Skewness-0.96694175
Sum23258
Variance172283.36
MonotonicityNot monotonic
2024-04-21T17:37:47.698031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1140 2
 
8.7%
1180 2
 
8.7%
590 1
 
4.3%
1087 1
 
4.3%
780 1
 
4.3%
752 1
 
4.3%
85 1
 
4.3%
1101 1
 
4.3%
970 1
 
4.3%
960 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
85 1
4.3%
155 1
4.3%
237 1
4.3%
590 1
4.3%
752 1
4.3%
780 1
4.3%
960 1
4.3%
970 1
4.3%
983 1
4.3%
1087 1
4.3%
ValueCountFrequency (%)
1695 1
4.3%
1496 1
4.3%
1384 1
4.3%
1380 1
4.3%
1295 1
4.3%
1240 1
4.3%
1218 1
4.3%
1210 1
4.3%
1180 2
8.7%
1140 2
8.7%

유타입(미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
0
21 
190
 
1
269
 
1

Length

Max length3
Median length1
Mean length1.173913
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row190
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
91.3%
190 1
 
4.3%
269 1
 
4.3%

Length

2024-04-21T17:37:47.936563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:37:48.115809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
91.3%
190 1
 
4.3%
269 1
 
4.3%

단선박스(미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
0
21 
142
 
1
21
 
1

Length

Max length3
Median length1
Mean length1.1304348
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row0
2nd row0
3rd row0
4th row142
5th row21

Common Values

ValueCountFrequency (%)
0 21
91.3%
142 1
 
4.3%
21 1
 
4.3%

Length

2024-04-21T17:37:48.309456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:37:48.489388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
91.3%
142 1
 
4.3%
21 1
 
4.3%

복선박스(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.82609
Minimum0
Maximum1266
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-04-21T17:37:48.655607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q1232
median673
Q3831.5
95-th percentile972.8
Maximum1266
Range1266
Interquartile range (IQR)599.5

Descriptive statistics

Standard deviation362.90252
Coefficient of variation (CV)0.63910858
Kurtosis-0.98011524
Mean567.82609
Median Absolute Deviation (MAD)207
Skewness-0.22245035
Sum13060
Variance131698.24
MonotonicityNot monotonic
2024-04-21T17:37:48.874125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12 2
 
8.7%
376 1
 
4.3%
797 1
 
4.3%
486 1
 
4.3%
519 1
 
4.3%
85 1
 
4.3%
880 1
 
4.3%
872 1
 
4.3%
238 1
 
4.3%
737 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 1
4.3%
12 2
8.7%
85 1
4.3%
155 1
4.3%
226 1
4.3%
238 1
4.3%
376 1
4.3%
486 1
4.3%
519 1
4.3%
623 1
4.3%
ValueCountFrequency (%)
1266 1
4.3%
982 1
4.3%
890 1
4.3%
880 1
4.3%
872 1
4.3%
835 1
4.3%
828 1
4.3%
811 1
4.3%
797 1
4.3%
757 1
4.3%

환기구(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.304348
Minimum0
Maximum128
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-04-21T17:37:49.091168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.5
median69
Q395
95-th percentile124.3
Maximum128
Range128
Interquartile range (IQR)61.5

Descriptive statistics

Standard deviation40.915028
Coefficient of variation (CV)0.65669619
Kurtosis-0.96782196
Mean62.304348
Median Absolute Deviation (MAD)28
Skewness-0.12054018
Sum1433
Variance1674.0395
MonotonicityNot monotonic
2024-04-21T17:37:49.295397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 4
17.4%
69 2
 
8.7%
24 1
 
4.3%
73 1
 
4.3%
25 1
 
4.3%
77 1
 
4.3%
98 1
 
4.3%
93 1
 
4.3%
48 1
 
4.3%
49 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
0 4
17.4%
24 1
 
4.3%
25 1
 
4.3%
42 1
 
4.3%
48 1
 
4.3%
49 1
 
4.3%
55 1
 
4.3%
56 1
 
4.3%
69 2
8.7%
73 1
 
4.3%
ValueCountFrequency (%)
128 1
4.3%
125 1
4.3%
118 1
4.3%
108 1
4.3%
98 1
4.3%
97 1
4.3%
93 1
4.3%
79 1
4.3%
77 1
4.3%
73 1
4.3%

단선정거장(미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
0
22 
66
 
1

Length

Max length2
Median length1
Mean length1.0434783
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22
95.7%
66 1
 
4.3%

Length

2024-04-21T17:37:49.509570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:37:49.670326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
95.7%
66 1
 
4.3%

복선정거장(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.56522
Minimum0
Maximum439
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-04-21T17:37:49.829193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1114
median161
Q3180
95-th percentile309
Maximum439
Range439
Interquartile range (IQR)66

Descriptive statistics

Standard deviation102.85331
Coefficient of variation (CV)0.67860758
Kurtosis1.8025847
Mean151.56522
Median Absolute Deviation (MAD)38
Skewness0.6859618
Sum3486
Variance10578.802
MonotonicityNot monotonic
2024-04-21T17:37:50.019875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 4
17.4%
165 2
 
8.7%
315 1
 
4.3%
156 1
 
4.3%
123 1
 
4.3%
175 1
 
4.3%
220 1
 
4.3%
174 1
 
4.3%
113 1
 
4.3%
185 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
0 4
17.4%
65 1
 
4.3%
113 1
 
4.3%
115 1
 
4.3%
123 1
 
4.3%
145 1
 
4.3%
148 1
 
4.3%
156 1
 
4.3%
161 1
 
4.3%
165 2
8.7%
ValueCountFrequency (%)
439 1
4.3%
315 1
4.3%
255 1
4.3%
220 1
4.3%
195 1
4.3%
185 1
4.3%
175 1
4.3%
174 1
4.3%
172 1
4.3%
165 2
8.7%

단선터널(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.26087
Minimum0
Maximum1457
Zeros18
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-04-21T17:37:50.194868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile884.5
Maximum1457
Range1457
Interquartile range (IQR)0

Descriptive statistics

Standard deviation371.64539
Coefficient of variation (CV)2.378365
Kurtosis6.7586573
Mean156.26087
Median Absolute Deviation (MAD)0
Skewness2.6236989
Sum3594
Variance138120.29
MonotonicityNot monotonic
2024-04-21T17:37:50.379174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
78.3%
610 1
 
4.3%
915 1
 
4.3%
105 1
 
4.3%
1457 1
 
4.3%
507 1
 
4.3%
ValueCountFrequency (%)
0 18
78.3%
105 1
 
4.3%
507 1
 
4.3%
610 1
 
4.3%
915 1
 
4.3%
1457 1
 
4.3%
ValueCountFrequency (%)
1457 1
 
4.3%
915 1
 
4.3%
610 1
 
4.3%
507 1
 
4.3%
105 1
 
4.3%
0 18
78.3%

복선터널(미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
0
19 
260
 
1
218
 
1
55
 
1
464
 
1

Length

Max length3
Median length1
Mean length1.3043478
Min length1

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row0
2nd row0
3rd row260
4th row218
5th row0

Common Values

ValueCountFrequency (%)
0 19
82.6%
260 1
 
4.3%
218 1
 
4.3%
55 1
 
4.3%
464 1
 
4.3%

Length

2024-04-21T17:37:50.624525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:37:50.830345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
82.6%
260 1
 
4.3%
218 1
 
4.3%
55 1
 
4.3%
464 1
 
4.3%

토피(미터)
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-04-21T17:37:51.351074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6521739
Min length7

Characters and Unicode

Total characters176
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row0.0~6.5
2nd row6.5~5.5
3rd row5.5~21.5
4th row4.0~17.5
5th row7.0~8.0
ValueCountFrequency (%)
2.5~10.5 2
 
8.7%
0.0~6.5 1
 
4.3%
6.5~5.5 1
 
4.3%
5.0~11.5 1
 
4.3%
5.0~8.5 1
 
4.3%
4.5~8.5 1
 
4.3%
2.0~9.5 1
 
4.3%
3.0~14.0 1
 
4.3%
2.5~9.8 1
 
4.3%
3.0~8.5 1
 
4.3%
Other values (12) 12
52.2%
2024-04-21T17:37:52.313148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 46
26.1%
5 33
18.8%
~ 23
13.1%
0 21
11.9%
1 19
10.8%
2 11
 
6.2%
4 5
 
2.8%
8 5
 
2.8%
3 5
 
2.8%
9 4
 
2.3%
Other values (2) 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107
60.8%
Other Punctuation 46
26.1%
Math Symbol 23
 
13.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 33
30.8%
0 21
19.6%
1 19
17.8%
2 11
 
10.3%
4 5
 
4.7%
8 5
 
4.7%
3 5
 
4.7%
9 4
 
3.7%
6 2
 
1.9%
7 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 46
100.0%
Math Symbol
ValueCountFrequency (%)
~ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 46
26.1%
5 33
18.8%
~ 23
13.1%
0 21
11.9%
1 19
10.8%
2 11
 
6.2%
4 5
 
2.8%
8 5
 
2.8%
3 5
 
2.8%
9 4
 
2.3%
Other values (2) 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 46
26.1%
5 33
18.8%
~ 23
13.1%
0 21
11.9%
1 19
10.8%
2 11
 
6.2%
4 5
 
2.8%
8 5
 
2.8%
3 5
 
2.8%
9 4
 
2.3%
Other values (2) 4
 
2.3%

Interactions

2024-04-21T17:37:43.099485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:39.282392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:40.032451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:41.364101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.402606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:43.256477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:39.437497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:40.386372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:41.620215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.547533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:43.404894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:39.588545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:40.628533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:41.867261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.688100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:43.555948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:39.737452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:40.876029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.113842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.826012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:43.692919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:39.877557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:41.109903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.247065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:37:42.954000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T17:37:52.479887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공구위치합계(미터)유타입(미터)단선박스(미터)복선박스(미터)환기구(미터)단선정거장(미터)복선정거장(미터)단선터널(미터)복선터널(미터)토피(미터)
공구1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계(미터)1.0001.0001.0000.8420.0000.6420.7971.0000.0000.6050.0000.947
유타입(미터)1.0001.0000.8421.0000.0001.0000.7780.0000.0000.0000.0001.000
단선박스(미터)1.0001.0000.0000.0001.0000.0000.0000.0000.0001.0000.6621.000
복선박스(미터)1.0001.0000.6421.0000.0001.0000.7370.0000.5670.0000.0000.932
환기구(미터)1.0001.0000.7970.7780.0000.7371.0000.0000.5980.0000.0000.932
단선정거장(미터)1.0001.0001.0000.0000.0000.0000.0001.0000.0001.0000.0001.000
복선정거장(미터)1.0001.0000.0000.0000.0000.5670.5980.0001.0000.0000.6480.921
단선터널(미터)1.0001.0000.6050.0001.0000.0000.0001.0000.0001.0000.6821.000
복선터널(미터)1.0001.0000.0000.0000.6620.0000.0000.0000.6480.6821.0001.000
토피(미터)1.0001.0000.9471.0001.0000.9320.9321.0000.9211.0001.0001.000
2024-04-21T17:37:52.709870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단선박스(미터)단선정거장(미터)복선터널(미터)유타입(미터)
단선박스(미터)1.0000.0000.5950.000
단선정거장(미터)0.0001.0000.0000.000
복선터널(미터)0.5950.0001.0000.000
유타입(미터)0.0000.0000.0001.000
2024-04-21T17:37:52.883730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계(미터)복선박스(미터)환기구(미터)복선정거장(미터)단선터널(미터)유타입(미터)단선박스(미터)단선정거장(미터)복선터널(미터)
합계(미터)1.0000.3210.4300.4650.4100.6800.0000.8450.000
복선박스(미터)0.3211.0000.5220.372-0.5840.8370.0000.0000.000
환기구(미터)0.4300.5221.0000.606-0.2030.3870.0000.0000.000
복선정거장(미터)0.4650.3720.6061.000-0.1180.0000.0000.0000.415
단선터널(미터)0.410-0.584-0.203-0.1181.0000.0000.9490.9260.302
유타입(미터)0.6800.8370.3870.0000.0001.0000.0000.0000.000
단선박스(미터)0.0000.0000.0000.0000.9490.0001.0000.0000.595
단선정거장(미터)0.8450.0000.0000.0000.9260.0000.0001.0000.000
복선터널(미터)0.0000.0000.0000.4150.3020.0000.5950.0001.000

Missing values

2024-04-21T17:37:43.912163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T17:37:44.203410image/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

공구위치합계(미터)유타입(미터)단선박스(미터)복선박스(미터)환기구(미터)단선정거장(미터)복선정거장(미터)단선터널(미터)복선터널(미터)토피(미터)
01공구기지∼판암(0k140∼0k730)59019003762400000.0~6.5
1위탁공구기지∼판암(0k730∼0k885)15500155000006.5~5.5
22공구판암∼신흥(0k885∼2k180)12950081179014502605.5~21.5
33공구신흥∼대동(2k180∼3k560)138001422266901156102184.0~17.5
44공구대동∼대전(3k560∼4k723)12100211297016591507.0~8.0
55공구대전∼중앙로(4k723∼4k960)23700120065105551.5~19.5
66공구대전∼중구청(4k960∼6k660)1695000066172145704.5~25.5
77공구중구청∼오룡(6k660∼7k900)13840067356014850702.5~19.0
88공구오룡∼용문(7k900∼9k400)1496001266690161004.5~12.0
99공구용문∼탄방(9k400∼10k540)114000890550195003.5~11.0
공구위치합계(미터)유타입(미터)단선박스(미터)복선박스(미터)환기구(미터)단선정거장(미터)복선정거장(미터)단선터널(미터)복선터널(미터)토피(미터)
1313공구월평∼갑천(14k130∼15k220)1087007971250165002.5~10.5
1414공구갑천∼유성온천(15k220∼16k460)124000982730185002.5~10.5
1515공구유성온천∼구암(16k460∼17k440)98300828420113003.0~8.5
1616공구구암∼현충원(17k440∼18k400)96000737490174002.5~9.8
1717공구현충원∼월드컵(18k400∼19k380)9700023848022004643.0~14.0
1818공구월드컵∼노은(19k380∼20k520)114000872930175002.0~9.5
1919공구노은∼지족(20k520∼21k621)110100880980123004.5~8.5
20반석천공구지족∼반석(21k621∼21k706)850085000005.0~8.5
2120공구지족∼반석(21k706∼22k480)75200519770156005.0~11.5
2221공구반석∼외삼기지(22k480∼23k260)78026904862500000.0~12.5