Overview

Dataset statistics

Number of variables11
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory104.0 B

Variable types

Text1
Numeric9
Categorical1

Dataset

Description대전교통공사 대전도시철도 1호선 부정승차 현황으로 역사별, 연도별, 자료를 제공합니다. 2022년 7월31일 기준 현황을 업데이트하여 제공합니다.
URLhttps://www.data.go.kr/data/15066378/fileData.do

Alerts

2014년 is highly overall correlated with 2015년High correlation
2015년 is highly overall correlated with 2014년 and 3 other fieldsHigh correlation
2016년 is highly overall correlated with 2017년 and 1 other fieldsHigh correlation
2017년 is highly overall correlated with 2015년 and 3 other fieldsHigh correlation
2018년 is highly overall correlated with 2015년 and 3 other fieldsHigh correlation
2019년 is highly overall correlated with 2015년 and 2 other fieldsHigh correlation
2021년 is highly overall correlated with 2022년High correlation
2022년 is highly overall correlated with 2021년High correlation
구분 has unique valuesUnique
2014년 has 1 (4.5%) zerosZeros
2015년 has 3 (13.6%) zerosZeros
2016년 has 4 (18.2%) zerosZeros
2017년 has 5 (22.7%) zerosZeros
2018년 has 6 (27.3%) zerosZeros
2019년 has 7 (31.8%) zerosZeros
2021년 has 11 (50.0%) zerosZeros
2022년 has 9 (40.9%) zerosZeros
2023년 7월 has 4 (18.2%) zerosZeros

Reproduction

Analysis started2023-12-12 20:25:47.255224
Analysis finished2023-12-12 20:25:56.159896
Duration8.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T05:25:56.290426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.6818182
Min length3

Characters and Unicode

Total characters81
Distinct characters50
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

Unique22 ?
Unique (%)100.0%

Sample

1st row판암역
2nd row신흥역
3rd row대동역
4th row대전역
5th row중앙로역
ValueCountFrequency (%)
판암역 1
 
4.5%
신흥역 1
 
4.5%
지족역 1
 
4.5%
노은역 1
 
4.5%
월드컵경기장역 1
 
4.5%
현충원역 1
 
4.5%
구암역 1
 
4.5%
유성온천역 1
 
4.5%
갑천역 1
 
4.5%
월평역 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T05:25:56.636464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 22
27.2%
대 3
 
3.7%
청 3
 
3.7%
중 2
 
2.5%
구 2
 
2.5%
암 2
 
2.5%
천 2
 
2.5%
전 2
 
2.5%
월 2
 
2.5%
현 1
 
1.2%
Other values (40) 40
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 22
27.2%
대 3
 
3.7%
청 3
 
3.7%
중 2
 
2.5%
구 2
 
2.5%
암 2
 
2.5%
천 2
 
2.5%
전 2
 
2.5%
월 2
 
2.5%
현 1
 
1.2%
Other values (40) 40
49.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 22
27.2%
대 3
 
3.7%
청 3
 
3.7%
중 2
 
2.5%
구 2
 
2.5%
암 2
 
2.5%
천 2
 
2.5%
전 2
 
2.5%
월 2
 
2.5%
현 1
 
1.2%
Other values (40) 40
49.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 22
27.2%
대 3
 
3.7%
청 3
 
3.7%
중 2
 
2.5%
구 2
 
2.5%
암 2
 
2.5%
천 2
 
2.5%
전 2
 
2.5%
월 2
 
2.5%
현 1
 
1.2%
Other values (40) 40
49.4%

2014년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.863636
Minimum0
Maximum82
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:56.785436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median12
Q322.75
95-th percentile54.85
Maximum82
Range82
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation21.85732
Coefficient of variation (CV)1.1587013
Kurtosis2.1470037
Mean18.863636
Median Absolute Deviation (MAD)10
Skewness1.5922473
Sum415
Variance477.74242
MonotonicityNot monotonic
2023-12-13T05:25:56.978818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 3
 
13.6%
2 3
 
13.6%
11 1
 
4.5%
20 1
 
4.5%
14 1
 
4.5%
23 1
 
4.5%
13 1
 
4.5%
82 1
 
4.5%
52 1
 
4.5%
28 1
 
4.5%
Other values (8) 8
36.4%
ValueCountFrequency (%)
0 1
 
4.5%
1 3
13.6%
2 3
13.6%
6 1
 
4.5%
8 1
 
4.5%
9 1
 
4.5%
11 1
 
4.5%
13 1
 
4.5%
14 1
 
4.5%
15 1
 
4.5%
ValueCountFrequency (%)
82 1
4.5%
55 1
4.5%
52 1
4.5%
48 1
4.5%
28 1
4.5%
23 1
4.5%
22 1
4.5%
20 1
4.5%
15 1
4.5%
14 1
4.5%

2015년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.454545
Minimum0
Maximum39
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:57.172454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median7.5
Q316.75
95-th percentile37.8
Maximum39
Range39
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.144869
Coefficient of variation (CV)1.0602663
Kurtosis0.70527451
Mean11.454545
Median Absolute Deviation (MAD)6.5
Skewness1.2406487
Sum252
Variance147.49784
MonotonicityNot monotonic
2023-12-13T05:25:57.342009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 3
13.6%
4 3
13.6%
1 3
13.6%
38 1
 
4.5%
16 1
 
4.5%
14 1
 
4.5%
9 1
 
4.5%
34 1
 
4.5%
39 1
 
4.5%
18 1
 
4.5%
Other values (6) 6
27.3%
ValueCountFrequency (%)
0 3
13.6%
1 3
13.6%
4 3
13.6%
5 1
 
4.5%
6 1
 
4.5%
9 1
 
4.5%
10 1
 
4.5%
12 1
 
4.5%
14 1
 
4.5%
16 1
 
4.5%
ValueCountFrequency (%)
39 1
4.5%
38 1
4.5%
34 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
14 1
4.5%
12 1
4.5%
10 1
4.5%

2016년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.727273
Minimum0
Maximum62
Zeros4
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:57.517674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8.5
Q314
95-th percentile49.9
Maximum62
Range62
Interquartile range (IQR)11

Descriptive statistics

Standard deviation16.403199
Coefficient of variation (CV)1.194935
Kurtosis3.2364114
Mean13.727273
Median Absolute Deviation (MAD)5.5
Skewness1.8386037
Sum302
Variance269.06494
MonotonicityNot monotonic
2023-12-13T05:25:57.693790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 4
18.2%
14 3
13.6%
3 2
 
9.1%
7 2
 
9.1%
62 1
 
4.5%
29 1
 
4.5%
25 1
 
4.5%
1 1
 
4.5%
11 1
 
4.5%
10 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
0 4
18.2%
1 1
 
4.5%
3 2
9.1%
5 1
 
4.5%
6 1
 
4.5%
7 2
9.1%
10 1
 
4.5%
11 1
 
4.5%
13 1
 
4.5%
14 3
13.6%
ValueCountFrequency (%)
62 1
 
4.5%
51 1
 
4.5%
29 1
 
4.5%
27 1
 
4.5%
25 1
 
4.5%
14 3
13.6%
13 1
 
4.5%
11 1
 
4.5%
10 1
 
4.5%
7 2
9.1%

2017년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0909091
Minimum0
Maximum30
Zeros5
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:57.843514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median3
Q37.75
95-th percentile23.95
Maximum30
Range30
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation9.0232984
Coefficient of variation (CV)1.2725164
Kurtosis0.91142618
Mean7.0909091
Median Absolute Deviation (MAD)3
Skewness1.4359184
Sum156
Variance81.419913
MonotonicityNot monotonic
2023-12-13T05:25:57.970889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5
22.7%
2 4
18.2%
3 3
13.6%
7 2
 
9.1%
23 1
 
4.5%
24 1
 
4.5%
16 1
 
4.5%
5 1
 
4.5%
18 1
 
4.5%
30 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 5
22.7%
1 1
 
4.5%
2 4
18.2%
3 3
13.6%
5 1
 
4.5%
7 2
 
9.1%
8 1
 
4.5%
16 1
 
4.5%
18 1
 
4.5%
23 1
 
4.5%
ValueCountFrequency (%)
30 1
 
4.5%
24 1
 
4.5%
23 1
 
4.5%
18 1
 
4.5%
16 1
 
4.5%
8 1
 
4.5%
7 2
9.1%
5 1
 
4.5%
3 3
13.6%
2 4
18.2%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7727273
Minimum0
Maximum19
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:58.098769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median2.5
Q310
95-th percentile18.8
Maximum19
Range19
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation6.6542634
Coefficient of variation (CV)1.152707
Kurtosis-0.63162513
Mean5.7727273
Median Absolute Deviation (MAD)2.5
Skewness0.90119272
Sum127
Variance44.279221
MonotonicityNot monotonic
2023-12-13T05:25:58.242613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 6
27.3%
1 4
18.2%
10 2
 
9.1%
15 2
 
9.1%
19 2
 
9.1%
13 1
 
4.5%
5 1
 
4.5%
2 1
 
4.5%
4 1
 
4.5%
8 1
 
4.5%
ValueCountFrequency (%)
0 6
27.3%
1 4
18.2%
2 1
 
4.5%
3 1
 
4.5%
4 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
10 2
 
9.1%
13 1
 
4.5%
15 2
 
9.1%
ValueCountFrequency (%)
19 2
9.1%
15 2
9.1%
13 1
 
4.5%
10 2
9.1%
8 1
 
4.5%
5 1
 
4.5%
4 1
 
4.5%
3 1
 
4.5%
2 1
 
4.5%
1 4
18.2%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2727273
Minimum0
Maximum15
Zeros7
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:58.402097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q34.5
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.8813574
Coefficient of variation (CV)1.1859703
Kurtosis2.7167221
Mean3.2727273
Median Absolute Deviation (MAD)2.5
Skewness1.6059526
Sum72
Variance15.064935
MonotonicityNot monotonic
2023-12-13T05:25:58.534564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 7
31.8%
3 5
22.7%
2 2
 
9.1%
1 2
 
9.1%
9 2
 
9.1%
5 1
 
4.5%
7 1
 
4.5%
15 1
 
4.5%
6 1
 
4.5%
ValueCountFrequency (%)
0 7
31.8%
1 2
 
9.1%
2 2
 
9.1%
3 5
22.7%
5 1
 
4.5%
6 1
 
4.5%
7 1
 
4.5%
9 2
 
9.1%
15 1
 
4.5%
ValueCountFrequency (%)
15 1
 
4.5%
9 2
 
9.1%
7 1
 
4.5%
6 1
 
4.5%
5 1
 
4.5%
3 5
22.7%
2 2
 
9.1%
1 2
 
9.1%
0 7
31.8%

2020년
Categorical

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
9 
1
7 
2
2 
3
2 
4
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 9
40.9%
1 7
31.8%
2 2
 
9.1%
3 2
 
9.1%
4 2
 
9.1%

Length

2023-12-13T05:25:58.694718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:58.831851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9
40.9%
1 7
31.8%
2 2
 
9.1%
3 2
 
9.1%
4 2
 
9.1%

2021년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1818182
Minimum0
Maximum9
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:58.967749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile4.9
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1299757
Coefficient of variation (CV)1.8022871
Kurtosis8.7842623
Mean1.1818182
Median Absolute Deviation (MAD)0.5
Skewness2.8290291
Sum26
Variance4.5367965
MonotonicityNot monotonic
2023-12-13T05:25:59.099422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11
50.0%
1 7
31.8%
3 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
2 1
 
4.5%
ValueCountFrequency (%)
0 11
50.0%
1 7
31.8%
2 1
 
4.5%
3 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
ValueCountFrequency (%)
9 1
 
4.5%
5 1
 
4.5%
3 1
 
4.5%
2 1
 
4.5%
1 7
31.8%
0 11
50.0%

2022년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3636364
Minimum0
Maximum59
Zeros9
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:59.233021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q39
95-th percentile52.1
Maximum59
Range59
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.195086
Coefficient of variation (CV)1.8363684
Kurtosis3.9295088
Mean9.3636364
Median Absolute Deviation (MAD)1.5
Skewness2.1926198
Sum206
Variance295.671
MonotonicityNot monotonic
2023-12-13T05:25:59.401172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 9
40.9%
2 2
 
9.1%
1 2
 
9.1%
3 1
 
4.5%
59 1
 
4.5%
4 1
 
4.5%
53 1
 
4.5%
10 1
 
4.5%
6 1
 
4.5%
14 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 9
40.9%
1 2
 
9.1%
2 2
 
9.1%
3 1
 
4.5%
4 1
 
4.5%
6 1
 
4.5%
10 1
 
4.5%
14 1
 
4.5%
16 1
 
4.5%
35 1
 
4.5%
ValueCountFrequency (%)
59 1
4.5%
53 1
4.5%
35 1
4.5%
16 1
4.5%
14 1
4.5%
10 1
4.5%
6 1
4.5%
4 1
4.5%
3 1
4.5%
2 2
9.1%

2023년 7월
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.454545
Minimum0
Maximum71
Zeros4
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:25:59.542105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median6
Q312.75
95-th percentile60.95
Maximum71
Range71
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation18.839491
Coefficient of variation (CV)1.5126598
Kurtosis5.8062248
Mean12.454545
Median Absolute Deviation (MAD)6
Skewness2.4763218
Sum274
Variance354.92641
MonotonicityNot monotonic
2023-12-13T05:25:59.708651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 4
18.2%
12 3
13.6%
3 2
9.1%
1 2
9.1%
6 2
9.1%
17 2
9.1%
4 1
 
4.5%
63 1
 
4.5%
22 1
 
4.5%
9 1
 
4.5%
Other values (3) 3
13.6%
ValueCountFrequency (%)
0 4
18.2%
1 2
9.1%
2 1
 
4.5%
3 2
9.1%
4 1
 
4.5%
6 2
9.1%
9 1
 
4.5%
12 3
13.6%
13 1
 
4.5%
17 2
9.1%
ValueCountFrequency (%)
71 1
 
4.5%
63 1
 
4.5%
22 1
 
4.5%
17 2
9.1%
13 1
 
4.5%
12 3
13.6%
9 1
 
4.5%
6 2
9.1%
4 1
 
4.5%
3 2
9.1%

Interactions

2023-12-13T05:25:55.092993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:47.712704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.624410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.894251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.788751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.503025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.214876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.893766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.653961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.174428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:47.805060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.709166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.982972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.869317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.572106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.283027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.958016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.743674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.269652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:47.918970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.138966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.078138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.964144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.655829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.370829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.043576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.866451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.369218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.012043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.244140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.192230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.054580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.734574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.447174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.118614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.994114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.454447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.125755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.364256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.289155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.126860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.820215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.517706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.183136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:54.107103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.549212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.223351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.475785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.386903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.210644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.902410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.603660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.262256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:54.648375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.661973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.317329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.582162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.490613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.287021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.990577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.676296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.341719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:54.748791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.741790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.399052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.687152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.591062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.351898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.062238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.739259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.428798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:54.849819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:55.827379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:48.490085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:49.779367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:50.683526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:51.424472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.139902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:52.815869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:53.545425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:54.960182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:25:59.849663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년 7월
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2014년1.0001.0000.7800.0000.1190.6820.8650.0000.0000.0000.000
2015년1.0000.7801.0000.0000.2570.6880.8920.2090.3690.3780.308
2016년1.0000.0000.0001.0000.8280.5080.7350.0000.0000.0000.000
2017년1.0000.1190.2570.8281.0000.9020.8630.0000.0000.0000.000
2018년1.0000.6820.6880.5080.9021.0000.6740.4650.0000.0000.000
2019년1.0000.8650.8920.7350.8630.6741.0000.0000.1740.0000.208
2020년1.0000.0000.2090.0000.0000.4650.0001.0000.5640.5960.459
2021년1.0000.0000.3690.0000.0000.0000.1740.5641.0000.8050.791
2022년1.0000.0000.3780.0000.0000.0000.0000.5960.8051.0000.947
2023년 7월1.0000.0000.3080.0000.0000.0000.2080.4590.7910.9471.000
2023-12-13T05:26:00.034182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2014년2015년2016년2017년2018년2019년2021년2022년2023년 7월2020년
2014년1.0000.5410.2690.2640.3690.412-0.0990.0050.0920.000
2015년0.5411.0000.4200.5950.5720.5830.1280.076-0.2240.019
2016년0.2690.4201.0000.6670.5070.4160.036-0.039-0.1510.000
2017년0.2640.5950.6671.0000.7360.817-0.025-0.132-0.2630.000
2018년0.3690.5720.5070.7361.0000.7420.107-0.085-0.1630.247
2019년0.4120.5830.4160.8170.7421.000-0.062-0.118-0.1180.000
2021년-0.0990.1280.036-0.0250.107-0.0621.0000.5770.1070.399
2022년0.0050.076-0.039-0.132-0.085-0.1180.5771.0000.3240.430
2023년 7월0.092-0.224-0.151-0.263-0.163-0.1180.1070.3241.0000.303
2020년0.0000.0190.0000.0000.2470.0000.3990.4300.3031.000

Missing values

2023-12-13T05:25:55.944621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:25:56.097552image/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

구분2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년 7월
0판암역113862231051020
1신흥역0629241530100
2대동역1030000004
3대전역22400000013
4중앙로역5512251613220212
5중구청역91015530001
6서대전네거리역61732003330
7오룡역15511181970510
8용문역207011115912
9탄방역818103232146
구분2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년 7월
12갈마역48192771994109
13월평역20165130491061
14갑천역2100000002
15유성온천역283968151500017
16구암역5234528311143
17현충원역824131310006
18월드컵경기장역13914210191617
19노은역231414312323571
20지족역111421000012
21반석역141770610013