Overview

Dataset statistics

Number of variables10
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory93.1 B

Variable types

Text1
Numeric9

Alerts

전용도로노선수(개소) is highly overall correlated with 전용도로연장(km) and 2 other fieldsHigh correlation
전용도로연장(km) is highly overall correlated with 전용도로노선수(개소)High correlation
보행자겸용도로노선수(개소) is highly overall correlated with 전용도로노선수(개소) and 1 other fieldsHigh correlation
보행자겸용도로연장(km) is highly overall correlated with 전용도로노선수(개소) and 1 other fieldsHigh correlation
전용차로노선수(개소) is highly overall correlated with 전용차로연장(km) and 1 other fieldsHigh correlation
전용차로연장(km) is highly overall correlated with 전용차로노선수(개소) and 1 other fieldsHigh correlation
자전거우선도로노선수(개소) is highly overall correlated with 자전거우선도로연장(km)High correlation
자전거우선도로연장(km) is highly overall correlated with 자전거우선도로노선수(개소)High correlation
자전거이용시설정비예산(백만원) is highly overall correlated with 전용차로노선수(개소) and 1 other fieldsHigh correlation
시군명 has unique valuesUnique
보행자겸용도로연장(km) has unique valuesUnique
전용도로노선수(개소) has 7 (21.9%) zerosZeros
전용도로연장(km) has 7 (21.9%) zerosZeros
전용차로노선수(개소) has 14 (43.8%) zerosZeros
전용차로연장(km) has 14 (43.8%) zerosZeros
자전거우선도로노선수(개소) has 20 (62.5%) zerosZeros
자전거우선도로연장(km) has 19 (59.4%) zerosZeros
자전거이용시설정비예산(백만원) has 1 (3.1%) zerosZeros

Reproduction

Analysis started2023-12-10 21:56:11.514614
Analysis finished2023-12-10 21:56:18.676343
Duration7.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T06:56:18.806192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.09375
Min length3

Characters and Unicode

Total characters99
Distinct characters41
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

Unique32 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row경기도
3rd row고양시
4th row과천시
5th row광명시
ValueCountFrequency (%)
가평군 1
 
3.1%
경기도 1
 
3.1%
하남시 1
 
3.1%
포천시 1
 
3.1%
평택시 1
 
3.1%
파주시 1
 
3.1%
이천시 1
 
3.1%
의정부시 1
 
3.1%
의왕시 1
 
3.1%
용인시 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T06:56:19.087136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
성 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
Other values (31) 35
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
성 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
Other values (31) 35
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
성 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
Other values (31) 35
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
성 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
Other values (31) 35
35.4%

전용도로노선수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4375
Minimum0
Maximum104
Zeros7
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:19.212833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q314
95-th percentile67.9
Maximum104
Range104
Interquartile range (IQR)13

Descriptive statistics

Standard deviation23.632861
Coefficient of variation (CV)1.7587245
Kurtosis7.4151981
Mean13.4375
Median Absolute Deviation (MAD)3.5
Skewness2.7072135
Sum430
Variance558.5121
MonotonicityNot monotonic
2023-12-11T06:56:19.329838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 7
21.9%
3 4
12.5%
1 3
 
9.4%
4 2
 
6.2%
2 2
 
6.2%
13 2
 
6.2%
7 1
 
3.1%
20 1
 
3.1%
22 1
 
3.1%
5 1
 
3.1%
Other values (8) 8
25.0%
ValueCountFrequency (%)
0 7
21.9%
1 3
9.4%
2 2
 
6.2%
3 4
12.5%
4 2
 
6.2%
5 1
 
3.1%
6 1
 
3.1%
7 1
 
3.1%
11 1
 
3.1%
13 2
 
6.2%
ValueCountFrequency (%)
104 1
3.1%
69 1
3.1%
67 1
3.1%
26 1
3.1%
23 1
3.1%
22 1
3.1%
20 1
3.1%
17 1
3.1%
13 2
6.2%
11 1
3.1%

전용도로연장(km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.64625
Minimum0
Maximum93.33
Zeros7
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:19.664184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.46
median7.01
Q328.3975
95-th percentile59.5715
Maximum93.33
Range93.33
Interquartile range (IQR)27.9375

Descriptive statistics

Standard deviation22.584337
Coefficient of variation (CV)1.2798378
Kurtosis3.2686967
Mean17.64625
Median Absolute Deviation (MAD)7.01
Skewness1.7499081
Sum564.68
Variance510.0523
MonotonicityNot monotonic
2023-12-11T06:56:19.759101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 7
21.9%
0.6 1
 
3.1%
23.37 1
 
3.1%
93.33 1
 
3.1%
5.2 1
 
3.1%
30.59 1
 
3.1%
16.0 1
 
3.1%
29.2 1
 
3.1%
26.46 1
 
3.1%
7.71 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
0.0 7
21.9%
0.43 1
 
3.1%
0.47 1
 
3.1%
0.6 1
 
3.1%
2.18 1
 
3.1%
4.0 1
 
3.1%
4.9 1
 
3.1%
5.2 1
 
3.1%
5.52 1
 
3.1%
6.31 1
 
3.1%
ValueCountFrequency (%)
93.33 1
3.1%
70.5 1
3.1%
50.63 1
3.1%
43.41 1
3.1%
39.87 1
3.1%
33.0 1
3.1%
30.59 1
3.1%
29.2 1
3.1%
28.13 1
3.1%
26.46 1
3.1%

보행자겸용도로노선수(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.625
Minimum1
Maximum640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:19.879347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.6
Q137.75
median77.5
Q3221
95-th percentile571.75
Maximum640
Range639
Interquartile range (IQR)183.25

Descriptive statistics

Standard deviation177.99923
Coefficient of variation (CV)1.1511672
Kurtosis1.6075044
Mean154.625
Median Absolute Deviation (MAD)62
Skewness1.5505123
Sum4948
Variance31683.726
MonotonicityNot monotonic
2023-12-11T06:56:19.988811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
49 2
 
6.2%
14 2
 
6.2%
109 1
 
3.1%
372 1
 
3.1%
16 1
 
3.1%
565 1
 
3.1%
101 1
 
3.1%
108 1
 
3.1%
112 1
 
3.1%
37 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
1 1
3.1%
2 1
3.1%
14 2
6.2%
15 1
3.1%
16 1
3.1%
31 1
3.1%
37 1
3.1%
38 1
3.1%
40 1
3.1%
41 1
3.1%
ValueCountFrequency (%)
640 1
3.1%
580 1
3.1%
565 1
3.1%
385 1
3.1%
372 1
3.1%
268 1
3.1%
249 1
3.1%
248 1
3.1%
212 1
3.1%
211 1
3.1%

보행자겸용도로연장(km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.96562
Minimum18.9
Maximum412.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:20.127631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.9
5-th percentile25.48
Q170.8
median99.8
Q3197.15
95-th percentile393.41
Maximum412.2
Range393.3
Interquartile range (IQR)126.35

Descriptive statistics

Standard deviation124.30136
Coefficient of variation (CV)0.82337526
Kurtosis-0.29009877
Mean150.96562
Median Absolute Deviation (MAD)46.25
Skewness1.05491
Sum4830.9
Variance15450.828
MonotonicityNot monotonic
2023-12-11T06:56:20.264031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
24.6 1
 
3.1%
143.3 1
 
3.1%
380.0 1
 
3.1%
91.9 1
 
3.1%
34.4 1
 
3.1%
412.2 1
 
3.1%
148.8 1
 
3.1%
77.5 1
 
3.1%
101.6 1
 
3.1%
69.3 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
18.9 1
3.1%
24.6 1
3.1%
26.2 1
3.1%
33.6 1
3.1%
34.4 1
3.1%
57.6 1
3.1%
63.7 1
3.1%
69.3 1
3.1%
71.3 1
3.1%
72.9 1
3.1%
ValueCountFrequency (%)
412.2 1
3.1%
409.8 1
3.1%
380.0 1
3.1%
361.8 1
3.1%
349.2 1
3.1%
319.9 1
3.1%
306.0 1
3.1%
235.1 1
3.1%
184.5 1
3.1%
170.1 1
3.1%

전용차로노선수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.25
Minimum0
Maximum26
Zeros14
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:20.363976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.25
95-th percentile14.7
Maximum26
Range26
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation5.7585841
Coefficient of variation (CV)1.771872
Kurtosis8.2187622
Mean3.25
Median Absolute Deviation (MAD)1
Skewness2.764136
Sum104
Variance33.16129
MonotonicityNot monotonic
2023-12-11T06:56:20.450481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14
43.8%
1 4
 
12.5%
3 4
 
12.5%
2 2
 
6.2%
4 2
 
6.2%
26 1
 
3.1%
12 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
18 1
 
3.1%
ValueCountFrequency (%)
0 14
43.8%
1 4
 
12.5%
2 2
 
6.2%
3 4
 
12.5%
4 2
 
6.2%
5 1
 
3.1%
6 1
 
3.1%
9 1
 
3.1%
12 1
 
3.1%
18 1
 
3.1%
ValueCountFrequency (%)
26 1
 
3.1%
18 1
 
3.1%
12 1
 
3.1%
9 1
 
3.1%
6 1
 
3.1%
5 1
 
3.1%
4 2
6.2%
3 4
12.5%
2 2
6.2%
1 4
12.5%

전용차로연장(km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.18875
Minimum0
Maximum75.9
Zeros14
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:20.537291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.385
Q37.1
95-th percentile58.317
Maximum75.9
Range75.9
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation19.37246
Coefficient of variation (CV)2.1082802
Kurtosis5.5982448
Mean9.18875
Median Absolute Deviation (MAD)0.385
Skewness2.509632
Sum294.04
Variance375.29219
MonotonicityNot monotonic
2023-12-11T06:56:20.633752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 14
43.8%
1.2 1
 
3.1%
2.3 1
 
3.1%
60.66 1
 
3.1%
7.4 1
 
3.1%
56.4 1
 
3.1%
1.04 1
 
3.1%
16.94 1
 
3.1%
27.38 1
 
3.1%
13.67 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
0.0 14
43.8%
0.34 1
 
3.1%
0.37 1
 
3.1%
0.4 1
 
3.1%
0.95 1
 
3.1%
0.98 1
 
3.1%
1.04 1
 
3.1%
1.2 1
 
3.1%
1.77 1
 
3.1%
2.3 1
 
3.1%
ValueCountFrequency (%)
75.9 1
3.1%
60.66 1
3.1%
56.4 1
3.1%
27.38 1
3.1%
19.34 1
3.1%
16.94 1
3.1%
13.67 1
3.1%
7.4 1
3.1%
7.0 1
3.1%
2.3 1
3.1%

자전거우선도로노선수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0625
Minimum0
Maximum13
Zeros20
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:20.719728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4.9
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.5392087
Coefficient of variation (CV)2.3898434
Kurtosis16.468407
Mean1.0625
Median Absolute Deviation (MAD)0
Skewness3.8345945
Sum34
Variance6.4475806
MonotonicityNot monotonic
2023-12-11T06:56:20.809647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 20
62.5%
1 7
 
21.9%
2 2
 
6.2%
13 1
 
3.1%
6 1
 
3.1%
4 1
 
3.1%
ValueCountFrequency (%)
0 20
62.5%
1 7
 
21.9%
2 2
 
6.2%
4 1
 
3.1%
6 1
 
3.1%
13 1
 
3.1%
ValueCountFrequency (%)
13 1
 
3.1%
6 1
 
3.1%
4 1
 
3.1%
2 2
 
6.2%
1 7
 
21.9%
0 20
62.5%

자전거우선도로연장(km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.371875
Minimum0
Maximum58
Zeros19
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:20.891970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.625
95-th percentile26.9595
Maximum58
Range58
Interquartile range (IQR)1.625

Descriptive statistics

Standard deviation12.047586
Coefficient of variation (CV)2.7557023
Kurtosis13.740195
Mean4.371875
Median Absolute Deviation (MAD)0
Skewness3.6241123
Sum139.9
Variance145.14433
MonotonicityNot monotonic
2023-12-11T06:56:20.974096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 19
59.4%
0.3 1
 
3.1%
20.79 1
 
3.1%
1.55 1
 
3.1%
6.7 1
 
3.1%
4.09 1
 
3.1%
34.5 1
 
3.1%
0.8 1
 
3.1%
1.85 1
 
3.1%
6.46 1
 
3.1%
Other values (4) 4
 
12.5%
ValueCountFrequency (%)
0.0 19
59.4%
0.3 1
 
3.1%
0.41 1
 
3.1%
0.7 1
 
3.1%
0.8 1
 
3.1%
1.55 1
 
3.1%
1.85 1
 
3.1%
3.75 1
 
3.1%
4.09 1
 
3.1%
6.46 1
 
3.1%
ValueCountFrequency (%)
58.0 1
3.1%
34.5 1
3.1%
20.79 1
3.1%
6.7 1
3.1%
6.46 1
3.1%
4.09 1
3.1%
3.75 1
3.1%
1.85 1
3.1%
1.55 1
3.1%
0.8 1
3.1%

자전거이용시설정비예산(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2762419 × 108
Minimum0
Maximum4.38 × 109
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T06:56:21.069417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18683250
Q11.305445 × 108
median3.430975 × 108
Q37.6373025 × 108
95-th percentile3.4255393 × 109
Maximum4.38 × 109
Range4.38 × 109
Interquartile range (IQR)6.3318575 × 108

Descriptive statistics

Standard deviation1.1775202 × 109
Coefficient of variation (CV)1.4227716
Kurtosis2.7518954
Mean8.2762419 × 108
Median Absolute Deviation (MAD)2.856685 × 108
Skewness1.9005075
Sum2.6483974 × 1010
Variance1.3865537 × 1018
MonotonicityNot monotonic
2023-12-11T06:56:21.165122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
300000000 2
 
6.2%
400000000 2
 
6.2%
452000000 1
 
3.1%
386195000 1
 
3.1%
2025000000 1
 
3.1%
131726000 1
 
3.1%
59822000 1
 
3.1%
127000000 1
 
3.1%
828825000 1
 
3.1%
250000000 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
0 1
3.1%
4155000 1
3.1%
30570000 1
3.1%
37475000 1
3.1%
45000000 1
3.1%
55036000 1
3.1%
59822000 1
3.1%
127000000 1
3.1%
131726000 1
3.1%
158728000 1
3.1%
ValueCountFrequency (%)
4380000000 1
3.1%
3945643000 1
3.1%
3000000000 1
3.1%
2570000000 1
3.1%
2400000000 1
3.1%
2025000000 1
3.1%
1139000000 1
3.1%
828825000 1
3.1%
742032000 1
3.1%
693586000 1
3.1%

Interactions

2023-12-11T06:56:17.738430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:11.804176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.492948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.171730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.928656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.891649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.579715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.322167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.019669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.822669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:11.880447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.573108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.261219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.012052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.959063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.664216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.407142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.099465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.907197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:11.953514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.637586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.335014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.100165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.027201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.751498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.477797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.179240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.983565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.026283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.720668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.410509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.184026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.106130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.828615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.558733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.265350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:18.065583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.105530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.795159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.496121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.269828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.198994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.931672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.638057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.358143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:18.138399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.176457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.869963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.575650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.562042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.273668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.006691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.714399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.432612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:18.213055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.253570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.942668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.665783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.657831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.349214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.088627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.798315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.510296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:18.285885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.325495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.018525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.759770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.739522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.420066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.169052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.872750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.585677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:18.371516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.405921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.099715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.851460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.817434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.508169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.246142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:16.949131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:17.658227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:56:21.234838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명전용도로노선수(개소)전용도로연장(km)보행자겸용도로노선수(개소)보행자겸용도로연장(km)전용차로노선수(개소)전용차로연장(km)자전거우선도로노선수(개소)자전거우선도로연장(km)자전거이용시설정비예산(백만원)
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전용도로노선수(개소)1.0001.0000.7550.8280.7200.4580.0000.0000.0000.468
전용도로연장(km)1.0000.7551.0000.5740.6330.6420.5710.0000.2560.553
보행자겸용도로노선수(개소)1.0000.8280.5741.0000.8750.7080.0000.0000.0000.746
보행자겸용도로연장(km)1.0000.7200.6330.8751.0000.0000.0000.6940.2520.386
전용차로노선수(개소)1.0000.4580.6420.7080.0001.0000.9110.0000.0000.855
전용차로연장(km)1.0000.0000.5710.0000.0000.9111.0000.0000.0000.520
자전거우선도로노선수(개소)1.0000.0000.0000.0000.6940.0000.0001.0000.9550.000
자전거우선도로연장(km)1.0000.0000.2560.0000.2520.0000.0000.9551.0000.355
자전거이용시설정비예산(백만원)1.0000.4680.5530.7460.3860.8550.5200.0000.3551.000
2023-12-11T06:56:21.367949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용도로노선수(개소)전용도로연장(km)보행자겸용도로노선수(개소)보행자겸용도로연장(km)전용차로노선수(개소)전용차로연장(km)자전거우선도로노선수(개소)자전거우선도로연장(km)자전거이용시설정비예산(백만원)
전용도로노선수(개소)1.0000.9130.6140.5660.3890.348-0.168-0.1350.347
전용도로연장(km)0.9131.0000.4250.4480.3370.308-0.1130.0000.328
보행자겸용도로노선수(개소)0.6140.4251.0000.8650.2440.130-0.008-0.0790.364
보행자겸용도로연장(km)0.5660.4480.8651.0000.2000.128-0.018-0.0510.315
전용차로노선수(개소)0.3890.3370.2440.2001.0000.969-0.069-0.0200.501
전용차로연장(km)0.3480.3080.1300.1280.9691.000-0.103-0.0360.514
자전거우선도로노선수(개소)-0.168-0.113-0.008-0.018-0.069-0.1031.0000.9080.180
자전거우선도로연장(km)-0.1350.000-0.079-0.051-0.020-0.0360.9081.0000.260
자전거이용시설정비예산(백만원)0.3470.3280.3640.3150.5010.5140.1800.2601.000

Missing values

2023-12-11T06:56:18.484290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:56:18.621479image/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

시군명전용도로노선수(개소)전용도로연장(km)보행자겸용도로노선수(개소)보행자겸용도로연장(km)전용차로노선수(개소)전용차로연장(km)자전거우선도로노선수(개소)자전거우선도로연장(km)자전거이용시설정비예산(백만원)
0가평군10.6224.611.210.3452000000
1경기도510.25875.72675.900.04380000000
2고양시2243.41248361.820.371320.792570000000
3과천시10.431433.600.000.037475000
4광명시00.03871.300.000.045000000
5광주시2039.875726.200.011.55300000000
6구리시00.04683.910.400.0162311000
7군포시00.04098.000.000.030570000
8김포시75.52183170.110.9816.73945643000
9남양주시42.18212235.110.3464.09196995000
시군명전용도로노선수(개소)전용도로연장(km)보행자겸용도로노선수(개소)보행자겸용도로연장(km)전용차로노선수(개소)전용차로연장(km)자전거우선도로노선수(개소)자전거우선도로연장(km)자전거이용시설정비예산(백만원)
22오산시1111.49211110.841.0410.41452835000
23용인시2328.13640306.000.000.03000000000
24의왕시37.713769.300.013.7555036000
25의정부시1326.46112101.600.000.0250000000
26이천시1729.210877.51856.400.0300000000
27파주시116.0101148.800.0158.0828825000
28평택시10430.59565412.237.400.0127000000
29포천시25.21634.400.000.059822000
30하남시00.04991.900.010.7131726000
31화성시6993.33372380.0960.6600.02025000000