Overview

Dataset statistics

Number of variables6
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory55.8 B

Variable types

Numeric5
Categorical1

Dataset

Description부산교통공사_열차운행실적_20201231
Author부산교통공사
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3057438

Alerts

계획(횟수) is highly overall correlated with 실적(횟수) and 3 other fieldsHigh correlation
실적(횟수) is highly overall correlated with 계획(횟수) and 3 other fieldsHigh correlation
계획(운행거리Km) is highly overall correlated with 계획(횟수) and 3 other fieldsHigh correlation
실적(운행거리Km) is highly overall correlated with 계획(횟수) and 3 other fieldsHigh correlation
호선별 is highly overall correlated with 계획(횟수) and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 16:15:26.175348
Analysis finished2023-12-10 16:15:29.249082
Duration3.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

월별
Real number (ℝ)

Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T01:15:29.328543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4885832
Coefficient of variation (CV)0.53670511
Kurtosis-1.2175129
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum312
Variance12.170213
MonotonicityIncreasing
2023-12-11T01:15:29.474315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 4
8.3%
2 4
8.3%
3 4
8.3%
4 4
8.3%
5 4
8.3%
6 4
8.3%
7 4
8.3%
8 4
8.3%
9 4
8.3%
10 4
8.3%
Other values (2) 8
16.7%
ValueCountFrequency (%)
1 4
8.3%
2 4
8.3%
3 4
8.3%
4 4
8.3%
5 4
8.3%
6 4
8.3%
7 4
8.3%
8 4
8.3%
9 4
8.3%
10 4
8.3%
ValueCountFrequency (%)
12 4
8.3%
11 4
8.3%
10 4
8.3%
9 4
8.3%
8 4
8.3%
7 4
8.3%
6 4
8.3%
5 4
8.3%
4 4
8.3%
3 4
8.3%

호선별
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
1호선
12 
2호선
12 
3호선
12 
4호선
12 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선
2nd row2호선
3rd row3호선
4th row4호선
5th row1호선

Common Values

ValueCountFrequency (%)
1호선 12
25.0%
2호선 12
25.0%
3호선 12
25.0%
4호선 12
25.0%

Length

2023-12-11T01:15:29.626516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:15:29.777713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 12
25.0%
2호선 12
25.0%
3호선 12
25.0%
4호선 12
25.0%

계획(횟수)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10014.271
Minimum8400
Maximum11736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T01:15:29.944560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8400
5-th percentile9014.6
Q19372.5
median9650
Q310700.5
95-th percentile11286.1
Maximum11736
Range3336
Interquartile range (IQR)1328

Descriptive statistics

Standard deviation829.6606
Coefficient of variation (CV)0.082847829
Kurtosis-0.98110374
Mean10014.271
Median Absolute Deviation (MAD)630
Skewness0.22835839
Sum480685
Variance688336.71
MonotonicityNot monotonic
2023-12-11T01:15:30.123248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10689 2
 
4.2%
9528 2
 
4.2%
10804 2
 
4.2%
9624 2
 
4.2%
9362 2
 
4.2%
9660 2
 
4.2%
9394 2
 
4.2%
10660 1
 
2.1%
9002 1
 
2.1%
9640 1
 
2.1%
Other values (31) 31
64.6%
ValueCountFrequency (%)
8400 1
2.1%
8628 1
2.1%
9002 1
2.1%
9038 1
2.1%
9052 1
2.1%
9102 1
2.1%
9116 1
2.1%
9284 1
2.1%
9348 1
2.1%
9356 1
2.1%
ValueCountFrequency (%)
11736 1
2.1%
11659 1
2.1%
11298 1
2.1%
11264 1
2.1%
11108 1
2.1%
10939 1
2.1%
10840 1
2.1%
10833 1
2.1%
10804 2
4.2%
10791 1
2.1%

실적(횟수)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10023.292
Minimum8400
Maximum11739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T01:15:30.332566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8400
5-th percentile9017.2
Q19379
median9651
Q310705.75
95-th percentile11289.35
Maximum11739
Range3339
Interquartile range (IQR)1326.75

Descriptive statistics

Standard deviation829.62799
Coefficient of variation (CV)0.082770013
Kurtosis-0.98122687
Mean10023.292
Median Absolute Deviation (MAD)629
Skewness0.21826106
Sum481118
Variance688282.59
MonotonicityNot monotonic
2023-12-11T01:15:30.524091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
9394 2
 
4.2%
10690 1
 
2.1%
10696 1
 
2.1%
11739 1
 
2.1%
10945 1
 
2.1%
9642 1
 
2.1%
9380 1
 
2.1%
11264 1
 
2.1%
10229 1
 
2.1%
9052 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
8400 1
2.1%
8628 1
2.1%
9006 1
2.1%
9038 1
2.1%
9052 1
2.1%
9110 1
2.1%
9116 1
2.1%
9337 1
2.1%
9348 1
2.1%
9362 1
2.1%
ValueCountFrequency (%)
11739 1
2.1%
11660 1
2.1%
11303 1
2.1%
11264 1
2.1%
11129 1
2.1%
10945 1
2.1%
10852 1
2.1%
10840 1
2.1%
10834 1
2.1%
10814 1
2.1%

계획(운행거리Km)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289842.8
Minimum100800
Maximum461533.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T01:15:30.723680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100800
5-th percentile108724.8
Q1145403.1
median290323
Q3441647
95-th percentile457642.72
Maximum461533.6
Range360733.6
Interquartile range (IQR)296243.9

Descriptive statistics

Standard deviation152921.46
Coefficient of variation (CV)0.52760139
Kurtosis-1.9741819
Mean289842.8
Median Absolute Deviation (MAD)151505.8
Skewness-0.042984904
Sum13912454
Variance2.3384974 × 1010
MonotonicityNot monotonic
2023-12-11T01:15:30.937458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
454667.8 2
 
4.2%
112344.0 2
 
4.2%
461533.6 2
 
4.2%
174194.4 2
 
4.2%
174846.0 2
 
4.2%
112728.0 2
 
4.2%
454313.4 1
 
2.1%
108024.0 1
 
2.1%
455460.8 1
 
2.1%
174484.0 1
 
2.1%
Other values (32) 32
66.7%
ValueCountFrequency (%)
100800.0 1
2.1%
108024.0 1
2.1%
108456.0 1
2.1%
109224.0 1
2.1%
109392.0 1
2.1%
112176.0 1
2.1%
112344.0 2
4.2%
112560.0 1
2.1%
112728.0 2
4.2%
113112.0 1
2.1%
ValueCountFrequency (%)
461533.6 2
4.2%
458817.6 1
2.1%
455460.8 1
2.1%
455409.4 1
2.1%
454667.8 2
4.2%
454313.4 1
2.1%
452124.6 1
2.1%
449997.6 1
2.1%
448136.0 1
2.1%
442192.4 1
2.1%

실적(운행거리Km)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290092.18
Minimum100800
Maximum461913.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T01:15:31.093824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100800
5-th percentile108758.4
Q1145403.1
median290393.4
Q3442465.38
95-th percentile457731.13
Maximum461913.6
Range361113.6
Interquartile range (IQR)297062.28

Descriptive statistics

Standard deviation153011.51
Coefficient of variation (CV)0.52745824
Kurtosis-1.9737484
Mean290092.18
Median Absolute Deviation (MAD)153039.65
Skewness-0.043719082
Sum13924425
Variance2.3412522 × 1010
MonotonicityNot monotonic
2023-12-11T01:15:31.235103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
112728.0 2
 
4.2%
454700.3 1
 
2.1%
455713.4 1
 
2.1%
432877.7 1
 
2.1%
455641.6 1
 
2.1%
174520.2 1
 
2.1%
112560.0 1
 
2.1%
415819.2 1
 
2.1%
426716.8 1
 
2.1%
167461.2 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
100800.0 1
2.1%
108072.0 1
2.1%
108456.0 1
2.1%
109320.0 1
2.1%
109392.0 1
2.1%
112176.0 1
2.1%
112344.0 1
2.1%
112440.0 1
2.1%
112560.0 1
2.1%
112728.0 2
4.2%
ValueCountFrequency (%)
461913.6 1
2.1%
461533.6 1
2.1%
458817.6 1
2.1%
455713.4 1
2.1%
455641.6 1
2.1%
454700.3 1
2.1%
454667.8 1
2.1%
454577.4 1
2.1%
452124.6 1
2.1%
449997.6 1
2.1%

Interactions

2023-12-11T01:15:28.427549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.420650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.857233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.362829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.899266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.532456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.521843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.935932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.455069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.994783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.647552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.612089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.017624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.546537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.112264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.789023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.690853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.126626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.642721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.204821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.910659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:26.775006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.250571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:27.774497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:15:28.323182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:15:31.329828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별호선별계획(횟수)실적(횟수)계획(운행거리Km)실적(운행거리Km)
월별1.0000.0000.0000.0000.0000.000
호선별0.0001.0000.7660.7890.8430.843
계획(횟수)0.0000.7661.0000.9990.7920.792
실적(횟수)0.0000.7890.9991.0000.7860.786
계획(운행거리Km)0.0000.8430.7920.7861.0001.000
실적(운행거리Km)0.0000.8430.7920.7861.0001.000
2023-12-11T01:15:31.802606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별계획(횟수)실적(횟수)계획(운행거리Km)실적(운행거리Km)호선별
월별1.0000.0920.129-0.056-0.0510.000
계획(횟수)0.0921.0000.9950.8560.8540.561
실적(횟수)0.1290.9951.0000.8530.8520.591
계획(운행거리Km)-0.0560.8560.8531.0000.9990.809
실적(운행거리Km)-0.0510.8540.8520.9991.0000.809
호선별0.0000.5610.5910.8090.8091.000

Missing values

2023-12-11T01:15:29.056399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:15:29.186579image/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

월별호선별계획(횟수)실적(횟수)계획(운행거리Km)실적(운행거리Km)
011호선1068910690454667.8454700.3
112호선1080410814461533.6461913.6
213호선96609668174846.0174986.8
314호선93949394112728.0112728.0
421호선95289528405800.0405800.0
522호선96299629411828.8411828.8
623호선86288628156166.8156166.8
724호선84008400100800.0100800.0
831호선1062210622452124.6452124.6
932호선1073510735458817.6458817.6
월별호선별계획(횟수)실적(횟수)계획(운행거리Km)실적(운행거리Km)
38103호선96249640174194.4174484.0
39104호선93629362112344.0112344.0
40111호선1079110852415764.6418161.5
41112호선1037810454442192.4445368.4
42113호선93569460169343.6171226.0
43114호선91029110109224.0109320.0
44121호선1110811129428048.0428863.7
45122호선1066010666454313.4454577.4
46123호선96249630174194.4174303.0
47124호선93629370112344.0112440.0