Overview

Dataset statistics

Number of variables9
Number of observations26
Missing cells49
Missing cells (%)20.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory86.1 B

Variable types

Numeric5
Categorical4

Dataset

Description매년 한국철도공사에서 발행하는 철도통계연보에 수록된 화차 하중별 보유현황으로 하중,종류,대수,단위 항목을 지원합니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15053621/fileData.do

Alerts

적재하중 is highly overall correlated with 소 화 물 and 2 other fieldsHigh correlation
보유량수 is highly overall correlated with 무 개 차 and 4 other fieldsHigh correlation
무 개 차 is highly overall correlated with 보유량수 and 3 other fieldsHigh correlation
평 판 차 is highly overall correlated with 보유량수 and 3 other fieldsHigh correlation
소 화 물 is highly overall correlated with 적재하중 and 4 other fieldsHigh correlation
유 개 차 is highly overall correlated with 적재하중 and 4 other fieldsHigh correlation
차 장 차 is highly overall correlated with 적재하중 and 1 other fieldsHigh correlation
유 개 차 is highly imbalanced (55.4%)Imbalance
유 조 차 is highly imbalanced (76.5%)Imbalance
차 장 차 is highly imbalanced (70.5%)Imbalance
침 식 차 is highly imbalanced (76.5%)Imbalance
무 개 차 has 19 (73.1%) missing valuesMissing
평 판 차 has 19 (73.1%) missing valuesMissing
소 화 물 has 11 (42.3%) missing valuesMissing
적재하중 has unique valuesUnique
적재하중 has 1 (3.8%) zerosZeros

Reproduction

Analysis started2023-12-12 09:05:23.270368
Analysis finished2023-12-12 09:05:26.700345
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

적재하중
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.623077
Minimum0
Maximum165
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:05:26.785608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.425
Q147.625
median53.4
Q359.125
95-th percentile97.5
Maximum165
Range165
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation30.663676
Coefficient of variation (CV)0.56136853
Kurtosis6.2863737
Mean54.623077
Median Absolute Deviation (MAD)6.25
Skewness1.7653374
Sum1420.2
Variance940.26105
MonotonicityStrictly increasing
2023-12-12T18:05:26.942671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 1
 
3.8%
54.0 1
 
3.8%
165.0 1
 
3.8%
100.0 1
 
3.8%
90.0 1
 
3.8%
70.0 1
 
3.8%
62.6 1
 
3.8%
61.0 1
 
3.8%
60.0 1
 
3.8%
56.5 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0.0 1
3.8%
15.0 1
3.8%
20.7 1
3.8%
25.0 1
3.8%
28.3 1
3.8%
40.0 1
3.8%
47.5 1
3.8%
48.0 1
3.8%
50.0 1
3.8%
51.0 1
3.8%
ValueCountFrequency (%)
165.0 1
3.8%
100.0 1
3.8%
90.0 1
3.8%
70.0 1
3.8%
62.6 1
3.8%
61.0 1
3.8%
60.0 1
3.8%
56.5 1
3.8%
55.8 1
3.8%
55.0 1
3.8%

보유량수
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.92308
Minimum1
Maximum2742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:05:27.093076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111.5
median50
Q3281.25
95-th percentile2062.5
Maximum2742
Range2741
Interquartile range (IQR)269.75

Descriptive statistics

Standard deviation700.72739
Coefficient of variation (CV)1.9968119
Kurtosis7.1343829
Mean350.92308
Median Absolute Deviation (MAD)49
Skewness2.7519748
Sum9124
Variance491018.87
MonotonicityNot monotonic
2023-12-12T18:05:27.250588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 3
 
11.5%
13 2
 
7.7%
3 2
 
7.7%
222 1
 
3.8%
122 1
 
3.8%
255 1
 
3.8%
20 1
 
3.8%
390 1
 
3.8%
115 1
 
3.8%
21 1
 
3.8%
Other values (12) 12
46.2%
ValueCountFrequency (%)
1 3
11.5%
3 2
7.7%
7 1
 
3.8%
11 1
 
3.8%
13 2
7.7%
20 1
 
3.8%
21 1
 
3.8%
35 1
 
3.8%
38 1
 
3.8%
62 1
 
3.8%
ValueCountFrequency (%)
2742 1
3.8%
2371 1
3.8%
1137 1
3.8%
591 1
3.8%
543 1
3.8%
390 1
3.8%
290 1
3.8%
255 1
3.8%
222 1
3.8%
122 1
3.8%

유 개 차
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
21 
11
 
1
516
 
1
243
 
1
114
 
1

Length

Max length4
Median length4
Mean length3.7307692
Min length2

Unique

Unique5 ?
Unique (%)19.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row11

Common Values

ValueCountFrequency (%)
<NA> 21
80.8%
11 1
 
3.8%
516 1
 
3.8%
243 1
 
3.8%
114 1
 
3.8%
30 1
 
3.8%

Length

2023-12-12T18:05:27.436608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:27.576683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
80.8%
11 1
 
3.8%
516 1
 
3.8%
243 1
 
3.8%
114 1
 
3.8%
30 1
 
3.8%

무 개 차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean364.14286
Minimum7
Maximum2126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:05:27.717532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10.9
Q121.5
median35
Q3169
95-th percentile1578.2
Maximum2126
Range2119
Interquartile range (IQR)147.5

Descriptive statistics

Standard deviation783.72219
Coefficient of variation (CV)2.1522383
Kurtosis6.6017941
Mean364.14286
Median Absolute Deviation (MAD)15
Skewness2.5556465
Sum2549
Variance614220.48
MonotonicityNot monotonic
2023-12-12T18:05:28.269503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
38 1
 
3.8%
35 1
 
3.8%
7 1
 
3.8%
300 1
 
3.8%
23 1
 
3.8%
2126 1
 
3.8%
20 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
7 1
3.8%
20 1
3.8%
23 1
3.8%
35 1
3.8%
38 1
3.8%
300 1
3.8%
2126 1
3.8%
ValueCountFrequency (%)
2126 1
3.8%
300 1
3.8%
38 1
3.8%
35 1
3.8%
23 1
3.8%
20 1
3.8%
7 1
3.8%

평 판 차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean403.28571
Minimum3
Maximum1023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:05:28.383085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.1
Q1102.5
median290
Q3651
95-th percentile1009.2
Maximum1023
Range1020
Interquartile range (IQR)548.5

Descriptive statistics

Standard deviation423.01328
Coefficient of variation (CV)1.0489171
Kurtosis-1.0532547
Mean403.28571
Median Absolute Deviation (MAD)200
Skewness0.94344714
Sum2823
Variance178940.24
MonotonicityNot monotonic
2023-12-12T18:05:28.532275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 1
 
3.8%
977 1
 
3.8%
325 1
 
3.8%
90 1
 
3.8%
290 1
 
3.8%
1023 1
 
3.8%
115 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
3 1
3.8%
90 1
3.8%
115 1
3.8%
290 1
3.8%
325 1
3.8%
977 1
3.8%
1023 1
3.8%
ValueCountFrequency (%)
1023 1
3.8%
977 1
3.8%
325 1
3.8%
290 1
3.8%
115 1
3.8%
90 1
3.8%
3 1
3.8%

소 화 물
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)86.7%
Missing11
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean184.2
Minimum1
Maximum1094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:05:28.671455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median102
Q3188.5
95-th percentile679.6
Maximum1094
Range1093
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation294.0292
Coefficient of variation (CV)1.5962497
Kurtosis6.5801088
Mean184.2
Median Absolute Deviation (MAD)99
Skewness2.4484042
Sum2763
Variance86453.171
MonotonicityNot monotonic
2023-12-12T18:05:28.802767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 3
 
11.5%
27 1
 
3.8%
1094 1
 
3.8%
117 1
 
3.8%
502 1
 
3.8%
102 1
 
3.8%
114 1
 
3.8%
21 1
 
3.8%
13 1
 
3.8%
390 1
 
3.8%
Other values (3) 3
 
11.5%
(Missing) 11
42.3%
ValueCountFrequency (%)
1 3
11.5%
3 1
 
3.8%
13 1
 
3.8%
21 1
 
3.8%
27 1
 
3.8%
102 1
 
3.8%
114 1
 
3.8%
117 1
 
3.8%
122 1
 
3.8%
255 1
 
3.8%
ValueCountFrequency (%)
1094 1
3.8%
502 1
3.8%
390 1
3.8%
255 1
3.8%
122 1
3.8%
117 1
3.8%
114 1
3.8%
102 1
3.8%
27 1
3.8%
21 1
3.8%

유 조 차
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
25 
16
 
1

Length

Max length4
Median length4
Mean length3.9230769
Min length2

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row<NA>
2nd row16
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 25
96.2%
16 1
 
3.8%

Length

2023-12-12T18:05:28.976004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:29.108930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
96.2%
16 1
 
3.8%

차 장 차
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
24 
13
 
1
22
 
1

Length

Max length4
Median length4
Mean length3.8461538
Min length2

Unique

Unique2 ?
Unique (%)7.7%

Sample

1st row13
2nd row22
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 24
92.3%
13 1
 
3.8%
22 1
 
3.8%

Length

2023-12-12T18:05:29.238494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:29.372724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
92.3%
13 1
 
3.8%
22 1
 
3.8%

침 식 차
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
25 
24
 
1

Length

Max length4
Median length4
Mean length3.9230769
Min length2

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row<NA>
2nd row24
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 25
96.2%
24 1
 
3.8%

Length

2023-12-12T18:05:29.543443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:29.650841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
96.2%
24 1
 
3.8%

Interactions

2023-12-12T18:05:25.656797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:23.619764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.100548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.585807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.072798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.764930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:23.701702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.183890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.684852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.192375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.892688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:23.777862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.259899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.773319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.294401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:26.003536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:23.945312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.403505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.862248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.444932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:26.104179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.026594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.499750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:24.968642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:25.565628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:05:29.726868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적재하중보유량수유 개 차무 개 차평 판 차소 화 물차 장 차
적재하중1.0000.0001.0000.0000.0000.000NaN
보유량수0.0001.0001.0001.0000.9360.827NaN
유 개 차1.0001.0001.0000.0000.0001.000NaN
무 개 차0.0001.0000.0001.0000.0000.000NaN
평 판 차0.0000.9360.0000.0001.0001.000NaN
소 화 물0.0000.8271.0000.0001.0001.000NaN
차 장 차NaNNaNNaNNaNNaNNaN1.000
2023-12-12T18:05:29.841099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
침 식 차유 조 차차 장 차유 개 차
침 식 차1.000NaNNaNNaN
유 조 차NaN1.000NaNNaN
차 장 차NaNNaN1.000NaN
유 개 차NaNNaNNaN1.000
2023-12-12T18:05:29.949455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적재하중보유량수무 개 차평 판 차소 화 물유 개 차유 조 차차 장 차침 식 차
적재하중1.000-0.189-0.0360.250-0.5591.000NaN1.000NaN
보유량수-0.1891.0000.8930.9290.8741.000NaN1.000NaN
무 개 차-0.0360.8931.0001.000-1.0001.0000.0000.0000.000
평 판 차0.2500.9291.0001.0000.5001.0000.0000.0000.000
소 화 물-0.5590.874-1.0000.5001.0001.0000.0000.0000.000
유 개 차1.0001.0001.0001.0001.0001.0000.0000.0000.000
유 조 차NaNNaN0.0000.0000.0000.0001.000NaNNaN
차 장 차1.0001.0000.0000.0000.0000.000NaN1.000NaN
침 식 차NaNNaN0.0000.0000.0000.000NaNNaN1.000

Missing values

2023-12-12T18:05:26.248031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:05:26.419204image/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.
2023-12-12T18:05:26.568259image/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

적재하중보유량수유 개 차무 개 차평 판 차소 화 물유 조 차차 장 차침 식 차
00.013<NA><NA><NA><NA><NA>13<NA>
115.062<NA><NA><NA><NA>162224
220.738<NA>38<NA><NA><NA><NA><NA>
325.035<NA>35<NA><NA><NA><NA><NA>
428.31111<NA><NA><NA><NA><NA><NA>
540.03<NA><NA>3<NA><NA><NA><NA>
647.57<NA>7<NA><NA><NA><NA><NA>
748.0543516<NA><NA>27<NA><NA><NA>
850.02371<NA>3009771094<NA><NA><NA>
951.059124323325<NA><NA><NA><NA>
적재하중보유량수유 개 차무 개 차평 판 차소 화 물유 조 차차 장 차침 식 차
1655.0115<NA><NA>115<NA><NA><NA><NA>
1755.813<NA><NA><NA>13<NA><NA><NA>
1856.5390<NA><NA><NA>390<NA><NA><NA>
1960.03<NA><NA><NA>3<NA><NA><NA>
2061.020<NA>20<NA><NA><NA><NA><NA>
2162.6255<NA><NA><NA>255<NA><NA><NA>
2270.0122<NA><NA><NA>122<NA><NA><NA>
2390.01<NA><NA><NA>1<NA><NA><NA>
24100.01<NA><NA><NA>1<NA><NA><NA>
25165.01<NA><NA><NA>1<NA><NA><NA>