Overview

Dataset statistics

Number of variables9
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory79.3 B

Variable types

Categorical3
Numeric6

Dataset

Description구미도시공사는 구미시에 공영유료주차장의 관리운영을 위탁받아 운영하고 있습니다. 주차장은 대분류로 8개소를 운영하며 일일 이용요금과 월정기 이용요금을 징수하여 구미시로 납부합니다. 일일 및 월정기 이용요금은 현금과 카드로 납부할 수 있으며, 주차장 인근 상가, 사무실 등 업소에서는 방문고객의 무료주차 제공을 위하여 상품권을 별도로 구매가능하며, 이 또한 현금과 카드로 납부가 가능합니다. 금오천주차장은 2022년도부터 신용카드전용주차장으로 운영하고 있으나 일부 카드를 소지하지 않은 고객은 별도로 현금징수한 경우도 있어 현금건수가 발생합니다.
Author구미시설공단
URLhttps://www.data.go.kr/data/15126162/fileData.do

Alerts

이용종료일 is highly overall correlated with 이용시작일High correlation
이용시작일 is highly overall correlated with 이용종료일High correlation
시간주차현금건수 is highly overall correlated with 주차장구분High correlation
시간주차카드건수 is highly overall correlated with 정기권판매카드건수 and 1 other fieldsHigh correlation
정기권판매현금건수 is highly overall correlated with 주차장구분High correlation
정기권판매카드건수 is highly overall correlated with 시간주차카드건수 and 3 other fieldsHigh correlation
상품권판매현금건수 is highly overall correlated with 정기권판매카드건수 and 1 other fieldsHigh correlation
상품권판매카드건수 is highly overall correlated with 정기권판매카드건수 and 1 other fieldsHigh correlation
주차장구분 is highly overall correlated with 시간주차현금건수 and 3 other fieldsHigh correlation
시간주차현금건수 has 7 (7.3%) zerosZeros
시간주차카드건수 has 2 (2.1%) zerosZeros
정기권판매현금건수 has 38 (39.6%) zerosZeros
정기권판매카드건수 has 61 (63.5%) zerosZeros
상품권판매현금건수 has 60 (62.5%) zerosZeros
상품권판매카드건수 has 71 (74.0%) zerosZeros

Reproduction

Analysis started2024-03-14 15:18:01.142633
Analysis finished2024-03-14 15:18:11.111317
Duration9.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이용시작일
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size896.0 B
2022-01-01
8 
2022-02-01
8 
2022-03-01
8 
2022-04-01
8 
2022-05-01
8 
Other values (7)
56 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01
2nd row2022-01-01
3rd row2022-01-01
4th row2022-01-01
5th row2022-01-01

Common Values

ValueCountFrequency (%)
2022-01-01 8
8.3%
2022-02-01 8
8.3%
2022-03-01 8
8.3%
2022-04-01 8
8.3%
2022-05-01 8
8.3%
2022-06-01 8
8.3%
2022-07-01 8
8.3%
2022-08-01 8
8.3%
2022-09-01 8
8.3%
2022-10-01 8
8.3%
Other values (2) 16
16.7%

Length

2024-03-15T00:18:11.337764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-01-01 8
8.3%
2022-02-01 8
8.3%
2022-03-01 8
8.3%
2022-04-01 8
8.3%
2022-05-01 8
8.3%
2022-06-01 8
8.3%
2022-07-01 8
8.3%
2022-08-01 8
8.3%
2022-09-01 8
8.3%
2022-10-01 8
8.3%
Other values (2) 16
16.7%

이용종료일
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size896.0 B
2022-01-31
8 
2022-02-28
8 
2022-03-31
8 
2022-04-30
8 
2022-05-31
8 
Other values (7)
56 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-31
2nd row2022-01-31
3rd row2022-01-31
4th row2022-01-31
5th row2022-01-31

Common Values

ValueCountFrequency (%)
2022-01-31 8
8.3%
2022-02-28 8
8.3%
2022-03-31 8
8.3%
2022-04-30 8
8.3%
2022-05-31 8
8.3%
2022-06-30 8
8.3%
2022-07-31 8
8.3%
2022-08-31 8
8.3%
2022-09-30 8
8.3%
2022-10-31 8
8.3%
Other values (2) 16
16.7%

Length

2024-03-15T00:18:11.713322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-01-31 8
8.3%
2022-02-28 8
8.3%
2022-03-31 8
8.3%
2022-04-30 8
8.3%
2022-05-31 8
8.3%
2022-06-30 8
8.3%
2022-07-31 8
8.3%
2022-08-31 8
8.3%
2022-09-30 8
8.3%
2022-10-31 8
8.3%
Other values (2) 16
16.7%

주차장구분
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size896.0 B
광평천
12 
구미시청
12 
금오산
12 
금오천
12 
단계천
12 
Other values (4)
36 

Length

Max length8
Median length7.5
Mean length4.1875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공단2동사무소앞
2nd row광평천
3rd row구미시청
4th row금오산
5th row금오천

Common Values

ValueCountFrequency (%)
광평천 12
12.5%
구미시청 12
12.5%
금오산 12
12.5%
금오천 12
12.5%
단계천 12
12.5%
문화예술회관 12
12.5%
원평가로 12
12.5%
공단2동사무소앞 6
6.2%
공단동사무소앞 6
6.2%

Length

2024-03-15T00:18:12.096055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:18:12.459329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광평천 12
12.5%
구미시청 12
12.5%
금오산 12
12.5%
금오천 12
12.5%
단계천 12
12.5%
문화예술회관 12
12.5%
원평가로 12
12.5%
공단2동사무소앞 6
6.2%
공단동사무소앞 6
6.2%

시간주차현금건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13152.958
Minimum0
Maximum50914
Zeros7
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size992.0 B
2024-03-15T00:18:12.888538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12999.5
median4318
Q318615.5
95-th percentile44142.25
Maximum50914
Range50914
Interquartile range (IQR)15616

Descriptive statistics

Standard deviation14646.603
Coefficient of variation (CV)1.1135596
Kurtosis0.21278421
Mean13152.958
Median Absolute Deviation (MAD)4318
Skewness1.1838407
Sum1262684
Variance2.1452298 × 108
MonotonicityNot monotonic
2024-03-15T00:18:13.331977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
7.3%
1 5
 
5.2%
2 3
 
3.1%
31377 1
 
1.0%
13250 1
 
1.0%
31747 1
 
1.0%
43304 1
 
1.0%
2763 1
 
1.0%
12868 1
 
1.0%
3472 1
 
1.0%
Other values (74) 74
77.1%
ValueCountFrequency (%)
0 7
7.3%
1 5
5.2%
2 3
3.1%
2594 1
 
1.0%
2696 1
 
1.0%
2727 1
 
1.0%
2763 1
 
1.0%
2791 1
 
1.0%
2825 1
 
1.0%
2902 1
 
1.0%
ValueCountFrequency (%)
50914 1
1.0%
49712 1
1.0%
49253 1
1.0%
49164 1
1.0%
46657 1
1.0%
43304 1
1.0%
42732 1
1.0%
42272 1
1.0%
36809 1
1.0%
36580 1
1.0%

시간주차카드건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3648.7292
Minimum0
Maximum60421
Zeros2
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size992.0 B
2024-03-15T00:18:13.738999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.5
Q1533.5
median1141.5
Q33506.5
95-th percentile11517.25
Maximum60421
Range60421
Interquartile range (IQR)2973

Descriptive statistics

Standard deviation7028.5775
Coefficient of variation (CV)1.9263084
Kurtosis44.872149
Mean3648.7292
Median Absolute Deviation (MAD)839
Skewness5.8116215
Sum350278
Variance49400901
MonotonicityNot monotonic
2024-03-15T00:18:14.175161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.1%
1237 1
 
1.0%
993 1
 
1.0%
69 1
 
1.0%
9311 1
 
1.0%
2373 1
 
1.0%
760 1
 
1.0%
11201 1
 
1.0%
450 1
 
1.0%
1364 1
 
1.0%
Other values (85) 85
88.5%
ValueCountFrequency (%)
0 2
2.1%
18 1
1.0%
24 1
1.0%
39 1
1.0%
53 1
1.0%
55 1
1.0%
57 1
1.0%
65 1
1.0%
69 1
1.0%
73 1
1.0%
ValueCountFrequency (%)
60421 1
1.0%
12432 1
1.0%
12332 1
1.0%
11882 1
1.0%
11599 1
1.0%
11490 1
1.0%
11341 1
1.0%
11201 1
1.0%
10990 1
1.0%
10184 1
1.0%

정기권판매현금건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.104167
Minimum0
Maximum646
Zeros38
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size992.0 B
2024-03-15T00:18:14.579401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q376.25
95-th percentile515.25
Maximum646
Range646
Interquartile range (IQR)76.25

Descriptive statistics

Standard deviation174.5744
Coefficient of variation (CV)1.9374731
Kurtosis2.5720838
Mean90.104167
Median Absolute Deviation (MAD)12
Skewness2.0560261
Sum8650
Variance30476.221
MonotonicityNot monotonic
2024-03-15T00:18:14.903851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 38
39.6%
9 3
 
3.1%
78 3
 
3.1%
80 3
 
3.1%
12 3
 
3.1%
515 2
 
2.1%
18 2
 
2.1%
506 2
 
2.1%
13 2
 
2.1%
20 2
 
2.1%
Other values (35) 36
37.5%
ValueCountFrequency (%)
0 38
39.6%
1 1
 
1.0%
3 1
 
1.0%
5 1
 
1.0%
7 1
 
1.0%
9 3
 
3.1%
10 1
 
1.0%
11 1
 
1.0%
12 3
 
3.1%
13 2
 
2.1%
ValueCountFrequency (%)
646 1
1.0%
520 1
1.0%
517 2
2.1%
516 1
1.0%
515 2
2.1%
513 1
1.0%
511 1
1.0%
510 1
1.0%
506 2
2.1%
453 1
1.0%

정기권판매카드건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.32292
Minimum0
Maximum734
Zeros61
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size992.0 B
2024-03-15T00:18:15.129965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3152.25
95-th percentile634.25
Maximum734
Range734
Interquartile range (IQR)152.25

Descriptive statistics

Standard deviation193.86699
Coefficient of variation (CV)1.7897135
Kurtosis3.4882211
Mean108.32292
Median Absolute Deviation (MAD)0
Skewness2.0855153
Sum10399
Variance37584.41
MonotonicityNot monotonic
2024-03-15T00:18:15.360609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 61
63.5%
147 2
 
2.1%
610 2
 
2.1%
151 2
 
2.1%
550 1
 
1.0%
152 1
 
1.0%
214 1
 
1.0%
4 1
 
1.0%
208 1
 
1.0%
143 1
 
1.0%
Other values (23) 23
 
24.0%
ValueCountFrequency (%)
0 61
63.5%
4 1
 
1.0%
139 1
 
1.0%
140 1
 
1.0%
143 1
 
1.0%
146 1
 
1.0%
147 2
 
2.1%
148 1
 
1.0%
151 2
 
2.1%
152 1
 
1.0%
ValueCountFrequency (%)
734 1
1.0%
714 1
1.0%
686 1
1.0%
659 1
1.0%
653 1
1.0%
628 1
1.0%
610 2
2.1%
550 1
1.0%
347 1
1.0%
218 1
1.0%

상품권판매현금건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.61458
Minimum0
Maximum2800
Zeros60
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size992.0 B
2024-03-15T00:18:15.582615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3500
95-th percentile2056.25
Maximum2800
Range2800
Interquartile range (IQR)500

Descriptive statistics

Standard deviation729.75786
Coefficient of variation (CV)1.7432691
Kurtosis2.2322524
Mean418.61458
Median Absolute Deviation (MAD)0
Skewness1.7987761
Sum40187
Variance532546.53
MonotonicityNot monotonic
2024-03-15T00:18:15.826581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 60
62.5%
1200 2
 
2.1%
200 2
 
2.1%
2050 2
 
2.1%
500 2
 
2.1%
1150 2
 
2.1%
725 1
 
1.0%
370 1
 
1.0%
2075 1
 
1.0%
1820 1
 
1.0%
Other values (22) 22
 
22.9%
ValueCountFrequency (%)
0 60
62.5%
100 1
 
1.0%
190 1
 
1.0%
200 2
 
2.1%
250 1
 
1.0%
290 1
 
1.0%
325 1
 
1.0%
370 1
 
1.0%
390 1
 
1.0%
400 1
 
1.0%
ValueCountFrequency (%)
2800 1
1.0%
2750 1
1.0%
2590 1
1.0%
2244 1
1.0%
2075 1
1.0%
2050 2
2.1%
1820 1
1.0%
1800 1
1.0%
1750 1
1.0%
1550 1
1.0%

상품권판매카드건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1143.3854
Minimum0
Maximum15439
Zeros71
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2024-03-15T00:18:16.095402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3499.25
95-th percentile8173
Maximum15439
Range15439
Interquartile range (IQR)499.25

Descriptive statistics

Standard deviation2770.6422
Coefficient of variation (CV)2.4231919
Kurtosis10.183055
Mean1143.3854
Median Absolute Deviation (MAD)0
Skewness3.1133138
Sum109765
Variance7676458.4
MonotonicityNot monotonic
2024-03-15T00:18:16.312282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 71
74.0%
1800 2
 
2.1%
2850 2
 
2.1%
4599 1
 
1.0%
2100 1
 
1.0%
499 1
 
1.0%
2800 1
 
1.0%
9769 1
 
1.0%
1400 1
 
1.0%
8164 1
 
1.0%
Other values (14) 14
 
14.6%
ValueCountFrequency (%)
0 71
74.0%
499 1
 
1.0%
500 1
 
1.0%
700 1
 
1.0%
1150 1
 
1.0%
1400 1
 
1.0%
1800 2
 
2.1%
1875 1
 
1.0%
2075 1
 
1.0%
2100 1
 
1.0%
ValueCountFrequency (%)
15439 1
1.0%
10673 1
1.0%
10495 1
1.0%
9769 1
1.0%
8200 1
1.0%
8164 1
1.0%
7074 1
1.0%
4599 1
1.0%
4458 1
1.0%
3550 1
1.0%

Interactions

2024-03-15T00:18:09.245797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:01.621621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:03.136997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:04.667661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:06.190168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:07.750334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:09.498868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:01.893509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:03.384640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:04.915762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:06.441379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:07.995597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:09.756446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:02.104478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:03.634278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:05.168386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:06.699598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:08.242403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:10.192614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:02.362006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:03.890094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:05.423445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:06.957505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:08.490340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:10.358755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:02.619817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:04.151489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:05.678329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:07.217546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:08.743757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:10.513811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:02.868778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:04.402351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:05.925875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:07.463346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:18:08.986081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:18:16.481214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이용시작일이용종료일주차장구분시간주차현금건수시간주차카드건수정기권판매현금건수정기권판매카드건수상품권판매현금건수상품권판매카드건수
이용시작일1.0001.0000.0000.0000.0000.0000.0000.0000.000
이용종료일1.0001.0000.0000.0000.0000.0000.0000.0000.000
주차장구분0.0000.0001.0000.9190.7120.8440.7480.6850.625
시간주차현금건수0.0000.0000.9191.0000.5710.7670.6730.5940.796
시간주차카드건수0.0000.0000.7120.5711.0000.7400.9070.6730.679
정기권판매현금건수0.0000.0000.8440.7670.7401.0000.7100.4460.444
정기권판매카드건수0.0000.0000.7480.6730.9070.7101.0000.7600.892
상품권판매현금건수0.0000.0000.6850.5940.6730.4460.7601.0000.819
상품권판매카드건수0.0000.0000.6250.7960.6790.4440.8920.8191.000
2024-03-15T00:18:16.688231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주차장구분이용종료일이용시작일
주차장구분1.0000.0000.000
이용종료일0.0001.0001.000
이용시작일0.0001.0001.000
2024-03-15T00:18:16.849600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간주차현금건수시간주차카드건수정기권판매현금건수정기권판매카드건수상품권판매현금건수상품권판매카드건수이용시작일이용종료일주차장구분
시간주차현금건수1.0000.2160.4350.0260.2170.4010.0000.0000.551
시간주차카드건수0.2161.0000.2460.7270.2310.4220.0000.0000.532
정기권판매현금건수0.4350.2461.0000.3880.2150.4000.0000.0000.603
정기권판매카드건수0.0260.7270.3881.0000.5680.6950.0000.0000.517
상품권판매현금건수0.2170.2310.2150.5681.0000.8330.0000.0000.394
상품권판매카드건수0.4010.4220.4000.6950.8331.0000.0000.0000.388
이용시작일0.0000.0000.0000.0000.0000.0001.0001.0000.000
이용종료일0.0000.0000.0000.0000.0000.0001.0001.0000.000
주차장구분0.5510.5320.6030.5170.3940.3880.0000.0001.000

Missing values

2024-03-15T00:18:10.735929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:18:10.976378image/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

이용시작일이용종료일주차장구분시간주차현금건수시간주차카드건수정기권판매현금건수정기권판매카드건수상품권판매현금건수상품권판매카드건수
02022-01-012022-01-31공단2동사무소앞3290300004000
12022-01-012022-01-31광평천42732859831659259010673
22022-01-012022-01-31구미시청30706834517000
32022-01-012022-01-31금오산1175719780000
42022-01-012022-01-31금오천18395102052000
52022-01-012022-01-31단계천2974180000
62022-01-012022-01-31문화예술회관4706124118000
72022-01-012022-01-31원평가로1216511588313914002075
82022-02-012022-02-28공단2동사무소앞2594289008300
92022-02-012022-02-28광평천3658072863562827504458
이용시작일이용종료일주차장구분시간주차현금건수시간주차카드건수정기권판매현금건수정기권판매카드건수상품권판매현금건수상품권판매카드건수
862022-11-012022-11-30문화예술회관399011720000
872022-11-012022-11-30원평가로1356317307715214432850
882022-12-012022-12-31공단동사무소앞3008630001900
892022-12-012022-12-31광평천060421646550200499
902022-12-012022-12-31구미시청36809909453000
912022-12-012022-12-31금오산1105124440000
922022-12-012022-12-31금오천098461318900
932022-12-012022-12-31단계천3322730000
942022-12-012022-12-31문화예술회관4373111942000
952022-12-012022-12-31원평가로1246018478215312502100