Overview

Dataset statistics

Number of variables13
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory122.5 B

Variable types

Categorical1
Numeric12

Dataset

Description대구교통공사 사업소별(청사, 월배기지, 안심기지, 문양기지, 칠곡기지, 범물기지, 123호선 역사별, 경산관리소, 사월관리소)에서 이용한 상수도 월별 사용 현황입니다.
URLhttps://www.data.go.kr/data/15006421/fileData.do

Alerts

구분월 is highly overall correlated with 1호선 역사(세제곱미터)High correlation
월배기지(세제곱미터) is highly overall correlated with 안심기지(세제곱미터) and 3 other fieldsHigh correlation
안심기지(세제곱미터) is highly overall correlated with 월배기지(세제곱미터) and 1 other fieldsHigh correlation
문양기지(세제곱미터) is highly overall correlated with 2호선 역사(세제곱미터) and 2 other fieldsHigh correlation
1호선 역사(세제곱미터) is highly overall correlated with 구분월 and 3 other fieldsHigh correlation
2호선 역사(세제곱미터) is highly overall correlated with 월배기지(세제곱미터) and 4 other fieldsHigh correlation
3호선 역사(세제곱미터) is highly overall correlated with 1호선 역사(세제곱미터) and 1 other fieldsHigh correlation
경산관리소(세제곱미터) is highly overall correlated with 문양기지(세제곱미터)High correlation
사월관리소(세제곱미터) is highly overall correlated with 월배기지(세제곱미터) and 2 other fieldsHigh correlation
연도 is highly overall correlated with 문양기지(세제곱미터)High correlation
월배기지(세제곱미터) has unique valuesUnique
안심기지(세제곱미터) has unique valuesUnique
문양기지(세제곱미터) has unique valuesUnique
1호선 역사(세제곱미터) has unique valuesUnique
2호선 역사(세제곱미터) has unique valuesUnique
3호선 역사(세제곱미터) has unique valuesUnique
종합청사(세제곱미터) has 1 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-12 18:30:06.460212
Analysis finished2023-12-12 18:30:23.147850
Duration16.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2021
12 
2022
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 12
50.0%
2022 12
50.0%

Length

2023-12-13T03:30:23.239954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:30:23.368433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 12
50.0%
2022 12
50.0%

구분월
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:23.506631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q13.75
median6.5
Q39.25
95-th percentile11.85
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5262987
Coefficient of variation (CV)0.54250749
Kurtosis-1.2156934
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum156
Variance12.434783
MonotonicityNot monotonic
2023-12-13T03:30:23.633417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 2
8.3%
2 2
8.3%
3 2
8.3%
4 2
8.3%
5 2
8.3%
6 2
8.3%
7 2
8.3%
8 2
8.3%
9 2
8.3%
10 2
8.3%
Other values (2) 4
16.7%
ValueCountFrequency (%)
1 2
8.3%
2 2
8.3%
3 2
8.3%
4 2
8.3%
5 2
8.3%
6 2
8.3%
7 2
8.3%
8 2
8.3%
9 2
8.3%
10 2
8.3%
ValueCountFrequency (%)
12 2
8.3%
11 2
8.3%
10 2
8.3%
9 2
8.3%
8 2
8.3%
7 2
8.3%
6 2
8.3%
5 2
8.3%
4 2
8.3%
3 2
8.3%

종합청사(세제곱미터)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1039.25
Minimum0
Maximum2617
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:23.810637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile785.85
Q1933.5
median1022.5
Q31088
95-th percentile1274.1
Maximum2617
Range2617
Interquartile range (IQR)154.5

Descriptive statistics

Standard deviation412.08233
Coefficient of variation (CV)0.39651896
Kurtosis11.009651
Mean1039.25
Median Absolute Deviation (MAD)90.5
Skewness1.8890464
Sum24942
Variance169811.85
MonotonicityNot monotonic
2023-12-13T03:30:24.267260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1021 2
 
8.3%
905 1
 
4.2%
943 1
 
4.2%
872 1
 
4.2%
860 1
 
4.2%
0 1
 
4.2%
2617 1
 
4.2%
1124 1
 
4.2%
946 1
 
4.2%
974 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0 1
4.2%
777 1
4.2%
836 1
4.2%
860 1
4.2%
872 1
4.2%
905 1
4.2%
943 1
4.2%
946 1
4.2%
974 1
4.2%
998 1
4.2%
ValueCountFrequency (%)
2617 1
4.2%
1287 1
4.2%
1201 1
4.2%
1168 1
4.2%
1141 1
4.2%
1124 1
4.2%
1076 1
4.2%
1048 1
4.2%
1044 1
4.2%
1032 1
4.2%

월배기지(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1609.3333
Minimum1129
Maximum2426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:24.386823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1129
5-th percentile1142.1
Q11362.25
median1653
Q31810.25
95-th percentile2072.35
Maximum2426
Range1297
Interquartile range (IQR)448

Descriptive statistics

Standard deviation332.74088
Coefficient of variation (CV)0.20675697
Kurtosis-0.087784209
Mean1609.3333
Median Absolute Deviation (MAD)264.5
Skewness0.48662327
Sum38624
Variance110716.49
MonotonicityNot monotonic
2023-12-13T03:30:24.536595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1750 1
 
4.2%
1366 1
 
4.2%
2077 1
 
4.2%
1829 1
 
4.2%
1804 1
 
4.2%
2046 1
 
4.2%
1733 1
 
4.2%
1701 1
 
4.2%
1417 1
 
4.2%
1283 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1129 1
4.2%
1140 1
4.2%
1154 1
4.2%
1283 1
4.2%
1337 1
4.2%
1351 1
4.2%
1366 1
4.2%
1367 1
4.2%
1405 1
4.2%
1417 1
4.2%
ValueCountFrequency (%)
2426 1
4.2%
2077 1
4.2%
2046 1
4.2%
1934 1
4.2%
1865 1
4.2%
1829 1
4.2%
1804 1
4.2%
1754 1
4.2%
1750 1
4.2%
1733 1
4.2%

안심기지(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1111.4583
Minimum659
Maximum1465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:24.671651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum659
5-th percentile910.45
Q11011.75
median1105.5
Q31214
95-th percentile1309.95
Maximum1465
Range806
Interquartile range (IQR)202.25

Descriptive statistics

Standard deviation168.68313
Coefficient of variation (CV)0.15176739
Kurtosis1.158827
Mean1111.4583
Median Absolute Deviation (MAD)103.5
Skewness-0.40948179
Sum26675
Variance28453.998
MonotonicityNot monotonic
2023-12-13T03:30:24.807197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1172 1
 
4.2%
659 1
 
4.2%
969 1
 
4.2%
1070 1
 
4.2%
1141 1
 
4.2%
1256 1
 
4.2%
1278 1
 
4.2%
1314 1
 
4.2%
1182 1
 
4.2%
1036 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
659 1
4.2%
901 1
4.2%
964 1
4.2%
969 1
4.2%
986 1
4.2%
993 1
4.2%
1018 1
4.2%
1031 1
4.2%
1033 1
4.2%
1036 1
4.2%
ValueCountFrequency (%)
1465 1
4.2%
1314 1
4.2%
1287 1
4.2%
1282 1
4.2%
1278 1
4.2%
1256 1
4.2%
1200 1
4.2%
1196 1
4.2%
1182 1
4.2%
1174 1
4.2%

문양기지(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2073.1667
Minimum1061
Maximum3166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:24.944874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1061
5-th percentile1149.55
Q11429.25
median2052
Q32706
95-th percentile3146.1
Maximum3166
Range2105
Interquartile range (IQR)1276.75

Descriptive statistics

Standard deviation706.45906
Coefficient of variation (CV)0.34076327
Kurtosis-1.3196149
Mean2073.1667
Median Absolute Deviation (MAD)673
Skewness0.15454009
Sum49756
Variance499084.41
MonotonicityNot monotonic
2023-12-13T03:30:25.051808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1061 1
 
4.2%
2002 1
 
4.2%
2687 1
 
4.2%
3156 1
 
4.2%
3166 1
 
4.2%
3090 1
 
4.2%
2937 1
 
4.2%
2948 1
 
4.2%
2763 1
 
4.2%
2573 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1061 1
4.2%
1141 1
4.2%
1198 1
4.2%
1229 1
4.2%
1249 1
4.2%
1304 1
4.2%
1471 1
4.2%
1679 1
4.2%
1737 1
4.2%
1819 1
4.2%
ValueCountFrequency (%)
3166 1
4.2%
3156 1
4.2%
3090 1
4.2%
2948 1
4.2%
2937 1
4.2%
2763 1
4.2%
2687 1
4.2%
2573 1
4.2%
2240 1
4.2%
2206 1
4.2%
Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.70833
Minimum477
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:25.161543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum477
5-th percentile514.45
Q1570.75
median673.5
Q3750.75
95-th percentile1227.95
Maximum1499
Range1022
Interquartile range (IQR)180

Descriptive statistics

Standard deviation244.00044
Coefficient of variation (CV)0.33210518
Kurtosis3.6439224
Mean734.70833
Median Absolute Deviation (MAD)91
Skewness1.8745979
Sum17633
Variance59536.216
MonotonicityNot monotonic
2023-12-13T03:30:25.275185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
745 2
 
8.3%
1086 1
 
4.2%
517 1
 
4.2%
800 1
 
4.2%
728 1
 
4.2%
659 1
 
4.2%
514 1
 
4.2%
715 1
 
4.2%
753 1
 
4.2%
626 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
477 1
4.2%
514 1
4.2%
517 1
4.2%
548 1
4.2%
552 1
4.2%
570 1
4.2%
571 1
4.2%
605 1
4.2%
620 1
4.2%
626 1
4.2%
ValueCountFrequency (%)
1499 1
4.2%
1253 1
4.2%
1086 1
4.2%
953 1
4.2%
800 1
4.2%
753 1
4.2%
750 1
4.2%
745 2
8.3%
728 1
4.2%
715 1
4.2%
Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.333333
Minimum35
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:25.383750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile40.75
Q165.75
median77.5
Q399.5
95-th percentile114.7
Maximum151
Range116
Interquartile range (IQR)33.75

Descriptive statistics

Standard deviation25.950901
Coefficient of variation (CV)0.31906846
Kurtosis1.0757928
Mean81.333333
Median Absolute Deviation (MAD)13
Skewness0.5667176
Sum1952
Variance673.44928
MonotonicityNot monotonic
2023-12-13T03:30:25.513843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
65 2
 
8.3%
35 1
 
4.2%
72 1
 
4.2%
81 1
 
4.2%
66 1
 
4.2%
75 1
 
4.2%
77 1
 
4.2%
82 1
 
4.2%
115 1
 
4.2%
40 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
35 1
4.2%
40 1
4.2%
45 1
4.2%
64 1
4.2%
65 2
8.3%
66 1
4.2%
71 1
4.2%
72 1
4.2%
74 1
4.2%
75 1
4.2%
ValueCountFrequency (%)
151 1
4.2%
115 1
4.2%
113 1
4.2%
105 1
4.2%
102 1
4.2%
101 1
4.2%
99 1
4.2%
97 1
4.2%
82 1
4.2%
81 1
4.2%

1호선 역사(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13672.708
Minimum9723
Maximum20010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:25.636145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9723
5-th percentile9976.4
Q111813
median13866
Q314405.25
95-th percentile19712.25
Maximum20010
Range10287
Interquartile range (IQR)2592.25

Descriptive statistics

Standard deviation2822.545
Coefficient of variation (CV)0.20643642
Kurtosis0.59891017
Mean13672.708
Median Absolute Deviation (MAD)1741.5
Skewness0.86783461
Sum328145
Variance7966760.2
MonotonicityNot monotonic
2023-12-13T03:30:25.761299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
14398 1
 
4.2%
12052 1
 
4.2%
14239 1
 
4.2%
13935 1
 
4.2%
20010 1
 
4.2%
19899 1
 
4.2%
13797 1
 
4.2%
13255 1
 
4.2%
14427 1
 
4.2%
10587 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
9723 1
4.2%
9938 1
4.2%
10194 1
4.2%
10587 1
4.2%
10994 1
4.2%
11741 1
4.2%
11837 1
4.2%
12052 1
4.2%
12197 1
4.2%
13255 1
4.2%
ValueCountFrequency (%)
20010 1
4.2%
19899 1
4.2%
18654 1
4.2%
16151 1
4.2%
14652 1
4.2%
14427 1
4.2%
14398 1
4.2%
14239 1
4.2%
14088 1
4.2%
14018 1
4.2%

2호선 역사(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5889.75
Minimum3829
Maximum9633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:25.894566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3829
5-th percentile4235.8
Q14686
median5212.5
Q37024.75
95-th percentile8640.95
Maximum9633
Range5804
Interquartile range (IQR)2338.75

Descriptive statistics

Standard deviation1618.8386
Coefficient of variation (CV)0.27485694
Kurtosis-0.42178742
Mean5889.75
Median Absolute Deviation (MAD)965.5
Skewness0.7613908
Sum141354
Variance2620638.5
MonotonicityNot monotonic
2023-12-13T03:30:26.006034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7004 1
 
4.2%
4835 1
 
4.2%
5529 1
 
4.2%
5315 1
 
4.2%
8708 1
 
4.2%
9633 1
 
4.2%
7378 1
 
4.2%
8261 1
 
4.2%
6357 1
 
4.2%
5110 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3829 1
4.2%
4231 1
4.2%
4263 1
4.2%
4272 1
4.2%
4355 1
4.2%
4641 1
4.2%
4701 1
4.2%
4774 1
4.2%
4835 1
4.2%
4903 1
4.2%
ValueCountFrequency (%)
9633 1
4.2%
8708 1
4.2%
8261 1
4.2%
7733 1
4.2%
7378 1
4.2%
7087 1
4.2%
7004 1
4.2%
6842 1
4.2%
6596 1
4.2%
6357 1
4.2%

3호선 역사(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5292.6667
Minimum4281
Maximum6093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:26.109107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4281
5-th percentile4423.65
Q14902.75
median5428.5
Q35696.25
95-th percentile6021.25
Maximum6093
Range1812
Interquartile range (IQR)793.5

Descriptive statistics

Standard deviation539.84576
Coefficient of variation (CV)0.10199882
Kurtosis-1.0216402
Mean5292.6667
Median Absolute Deviation (MAD)395
Skewness-0.36663873
Sum127024
Variance291433.45
MonotonicityNot monotonic
2023-12-13T03:30:26.226126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5915 1
 
4.2%
4955 1
 
4.2%
5857 1
 
4.2%
5565 1
 
4.2%
5475 1
 
4.2%
5769 1
 
4.2%
6040 1
 
4.2%
6093 1
 
4.2%
5790 1
 
4.2%
4746 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
4281 1
4.2%
4413 1
4.2%
4484 1
4.2%
4619 1
4.2%
4742 1
4.2%
4746 1
4.2%
4955 1
4.2%
4961 1
4.2%
5083 1
4.2%
5199 1
4.2%
ValueCountFrequency (%)
6093 1
4.2%
6040 1
4.2%
5915 1
4.2%
5857 1
4.2%
5790 1
4.2%
5769 1
4.2%
5672 1
4.2%
5654 1
4.2%
5597 1
4.2%
5565 1
4.2%

경산관리소(세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.25
Minimum53
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:26.370407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile62.3
Q166.5
median70
Q374.25
95-th percentile88.4
Maximum89
Range36
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation8.5579864
Coefficient of variation (CV)0.12011209
Kurtosis0.66079517
Mean71.25
Median Absolute Deviation (MAD)4.5
Skewness0.58361582
Sum1710
Variance73.23913
MonotonicityNot monotonic
2023-12-13T03:30:26.487595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
71 4
16.7%
70 3
12.5%
67 2
 
8.3%
64 2
 
8.3%
65 2
 
8.3%
89 2
 
8.3%
53 1
 
4.2%
68 1
 
4.2%
69 1
 
4.2%
62 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
53 1
 
4.2%
62 1
 
4.2%
64 2
8.3%
65 2
8.3%
67 2
8.3%
68 1
 
4.2%
69 1
 
4.2%
70 3
12.5%
71 4
16.7%
74 1
 
4.2%
ValueCountFrequency (%)
89 2
8.3%
85 1
 
4.2%
84 1
 
4.2%
76 1
 
4.2%
75 1
 
4.2%
74 1
 
4.2%
71 4
16.7%
70 3
12.5%
69 1
 
4.2%
68 1
 
4.2%

사월관리소(세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.083333
Minimum54
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T03:30:26.605986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile68.15
Q178.25
median84
Q3101.25
95-th percentile122.35
Maximum129
Range75
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.607891
Coefficient of variation (CV)0.21125326
Kurtosis-0.029724107
Mean88.083333
Median Absolute Deviation (MAD)12
Skewness0.59233256
Sum2114
Variance346.25362
MonotonicityNot monotonic
2023-12-13T03:30:26.815582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
80 4
16.7%
69 2
 
8.3%
85 2
 
8.3%
111 1
 
4.2%
113 1
 
4.2%
92 1
 
4.2%
129 1
 
4.2%
89 1
 
4.2%
54 1
 
4.2%
71 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
54 1
 
4.2%
68 1
 
4.2%
69 2
8.3%
71 1
 
4.2%
73 1
 
4.2%
80 4
16.7%
82 1
 
4.2%
83 1
 
4.2%
85 2
8.3%
88 1
 
4.2%
ValueCountFrequency (%)
129 1
4.2%
124 1
4.2%
113 1
4.2%
111 1
4.2%
106 1
4.2%
102 1
4.2%
101 1
4.2%
92 1
4.2%
89 1
4.2%
88 1
4.2%

Interactions

2023-12-13T03:30:21.314354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:06.881256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.940857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.077865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.638608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.812882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.967164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.131769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.441456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.877349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.606458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.915785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.439758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:06.967030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.047563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.166460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.728332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.899979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.061235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.237092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.531070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.968544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.711748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.033758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.558163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.045646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.128296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.255543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.840237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.990568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.144098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.343670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.648503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:17.433605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.813871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.128815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.682830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.146216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.222081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.361150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.966168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.085445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.245760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.453611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.784650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:17.539837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.928552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.253704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.840567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.252439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.321130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.473831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.076119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.176260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.339969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.609946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.904470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:17.676878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.029915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.400995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.945496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.344276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.398110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.563422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.164392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.258627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.433481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.710837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.990974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:17.792614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.123698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.498809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:22.053601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.421574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.479136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.649309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.252710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.400987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.511219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.814848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.092581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:17.905952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.223997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.595082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:22.167114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.508268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.573154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.743667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.338066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.481660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.608495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.920773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.213155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.009331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.334283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.701415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:22.295819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.595644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.659578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:09.896731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.424943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.571651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.704022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.024936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.333595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.105891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.446959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.832570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:22.391291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.677976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.770416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.282067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.510712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.656033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.790918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.137315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.459064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.197948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.559643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:20.953086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:22.532641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.767401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.888273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.408559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.606105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.742928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:13.915457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.234716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.592037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.357451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.693388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.059632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:22.650044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:07.853256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:08.987806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:10.535930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:11.701744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:12.832393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:14.012209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:15.331614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:16.734390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:18.493450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:19.807641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:21.174374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:30:26.921872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분월종합청사(세제곱미터)월배기지(세제곱미터)안심기지(세제곱미터)문양기지(세제곱미터)칠곡기지(세제곱미터)범물기지(세제곱미터)1호선 역사(세제곱미터)2호선 역사(세제곱미터)3호선 역사(세제곱미터)경산관리소(세제곱미터)사월관리소(세제곱미터)
연도1.0000.0000.3100.3310.3340.8720.0000.0000.0000.4300.5630.5360.000
구분월0.0001.0000.7910.3230.6680.0000.3930.5360.7340.5600.4460.2580.628
종합청사(세제곱미터)0.3100.7911.0000.0000.0000.0000.0000.5140.5640.6870.0000.0000.427
월배기지(세제곱미터)0.3310.3230.0001.0000.6500.8660.0000.4190.7840.6500.8130.5580.000
안심기지(세제곱미터)0.3340.6680.0000.6501.0000.7180.0000.8630.6010.7070.0000.6260.000
문양기지(세제곱미터)0.8720.0000.0000.8660.7181.0000.0000.0000.6670.7370.4650.6290.361
칠곡기지(세제곱미터)0.0000.3930.0000.0000.0000.0001.0000.0000.1290.0000.5500.7780.000
범물기지(세제곱미터)0.0000.5360.5140.4190.8630.0000.0001.0000.3290.5710.0000.3750.398
1호선 역사(세제곱미터)0.0000.7340.5640.7840.6010.6670.1290.3291.0000.5970.7360.3890.561
2호선 역사(세제곱미터)0.4300.5600.6870.6500.7070.7370.0000.5710.5971.0000.0000.2460.509
3호선 역사(세제곱미터)0.5630.4460.0000.8130.0000.4650.5500.0000.7360.0001.0000.2620.378
경산관리소(세제곱미터)0.5360.2580.0000.5580.6260.6290.7780.3750.3890.2460.2621.0000.483
사월관리소(세제곱미터)0.0000.6280.4270.0000.0000.3610.0000.3980.5610.5090.3780.4831.000
2023-12-13T03:30:27.098775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분월종합청사(세제곱미터)월배기지(세제곱미터)안심기지(세제곱미터)문양기지(세제곱미터)칠곡기지(세제곱미터)범물기지(세제곱미터)1호선 역사(세제곱미터)2호선 역사(세제곱미터)3호선 역사(세제곱미터)경산관리소(세제곱미터)사월관리소(세제곱미터)연도
구분월1.0000.1740.3780.1390.474-0.0840.0620.5270.2530.3330.0180.1190.000
종합청사(세제곱미터)0.1741.0000.0920.345-0.112-0.3980.0460.2150.1660.205-0.3950.3570.342
월배기지(세제곱미터)0.3780.0921.0000.5500.3550.114-0.1410.5890.5800.432-0.1440.5510.318
안심기지(세제곱미터)0.1390.3450.5501.0000.1660.0520.1490.3560.4480.462-0.3300.5370.287
문양기지(세제곱미터)0.474-0.1120.3550.1661.000-0.3170.1190.3670.5430.3860.5050.0880.752
칠곡기지(세제곱미터)-0.084-0.3980.1140.052-0.3171.000-0.041-0.061-0.0690.047-0.2820.1270.000
범물기지(세제곱미터)0.0620.046-0.1410.1490.119-0.0411.0000.007-0.096-0.008-0.026-0.0100.000
1호선 역사(세제곱미터)0.5270.2150.5890.3560.367-0.0610.0071.0000.6670.627-0.0360.2500.000
2호선 역사(세제곱미터)0.2530.1660.5800.4480.543-0.069-0.0960.6671.0000.696-0.0030.5260.340
3호선 역사(세제곱미터)0.3330.2050.4320.4620.3860.047-0.0080.6270.6961.0000.0490.4110.236
경산관리소(세제곱미터)0.018-0.395-0.144-0.3300.505-0.282-0.026-0.036-0.0030.0491.000-0.4310.328
사월관리소(세제곱미터)0.1190.3570.5510.5370.0880.127-0.0100.2500.5260.411-0.4311.0000.000
연도0.0000.3420.3180.2870.7520.0000.0000.0000.3400.2360.3280.0001.000

Missing values

2023-12-13T03:30:22.812335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:30:23.033748image/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

연도구분월종합청사(세제곱미터)월배기지(세제곱미터)안심기지(세제곱미터)문양기지(세제곱미터)칠곡기지(세제곱미터)범물기지(세제곱미터)1호선 역사(세제곱미터)2호선 역사(세제곱미터)3호선 역사(세제곱미터)경산관리소(세제곱미터)사월관리소(세제곱미터)
02021190517501172106110863514398700459155388
120212943140510311229125374109944997461968102
220213998133798611411499659938427244136782
3202148361427128212499531059723382944847073
42021510481140103311986869711837423150836485
520216114117241196147175010114652477456546980
620217116817541068130474579133847087551967124
720218107618651287182166171121974641538270106
820219128724261465167955245161516596521362101
9202110120119341174224060511318654684249616483
연도구분월종합청사(세제곱미터)월배기지(세제곱미터)안심기지(세제곱미터)문양기지(세제곱미터)칠곡기지(세제곱미터)범물기지(세제곱미터)1호선 역사(세제곱미터)2호선 역사(세제곱미터)3호선 역사(세제곱미터)경산관리소(세제곱미터)사월관리소(세제곱미터)
14202231024135190121024776411741426347428569
15202247771367964220657015110194435542817171
16202259741283103625736264010587511047467454
172022694614171182276375311514427635757908980
182022711241701131429487158213255826160936589
1920228261717331278293751477137977378604071129
202022902046125630906597519899963357697685
2120221010211804114131667286620010870854757192
2220221186018291070315674581139355315556570113
23202212872207796926878006514239552958578480