Overview

Dataset statistics

Number of variables15
Number of observations155
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.0 KiB
Average record size in memory131.9 B

Variable types

DateTime2
Categorical2
Numeric11

Dataset

Description충청북도 충주시 읍면동별 자동차 등록현황에 대한 데이터 제공(구분 비사업용/사업용, 승용/승합/화물/특수, 경형/소형/중형/대형)
Author충청북도 충주시
URLhttps://www.data.go.kr/data/15029365/fileData.do

Alerts

통계기준년월 has constant value ""Constant
데이터기준일자 has constant value ""Constant
is highly overall correlated with 비사업용 소계 and 7 other fieldsHigh correlation
비사업용 소계 is highly overall correlated with and 7 other fieldsHigh correlation
비사업용 경형 is highly overall correlated with and 4 other fieldsHigh correlation
비사업용 소형 is highly overall correlated with and 6 other fieldsHigh correlation
비사업용 중형 is highly overall correlated with and 4 other fieldsHigh correlation
비사업용 대형 is highly overall correlated with and 6 other fieldsHigh correlation
사업용 소계 is highly overall correlated with and 6 other fieldsHigh correlation
사업용 소형 is highly overall correlated with 비사업용 소형 and 2 other fieldsHigh correlation
사업용 중형 is highly overall correlated with and 7 other fieldsHigh correlation
사업용 대형 is highly overall correlated with and 5 other fieldsHigh correlation
비사업용 경형 has 46 (29.7%) zerosZeros
비사업용 소형 has 13 (8.4%) zerosZeros
비사업용 중형 has 16 (10.3%) zerosZeros
비사업용 대형 has 32 (20.6%) zerosZeros
사업용 소계 has 43 (27.7%) zerosZeros
사업용 경형 has 146 (94.2%) zerosZeros
사업용 소형 has 108 (69.7%) zerosZeros
사업용 중형 has 62 (40.0%) zerosZeros
사업용 대형 has 61 (39.4%) zerosZeros

Reproduction

Analysis started2023-12-12 02:24:53.295538
Analysis finished2023-12-12 02:25:07.397192
Duration14.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계기준년월
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-09-01 00:00:00
Maximum2023-09-01 00:00:00
2023-12-12T11:25:07.439993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:07.532609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

읍면동
Categorical

Distinct40
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
합계
 
4
성내동
 
4
대소원면
 
4
가주동
 
4
성남동
 
4
Other values (35)
135 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row합계
2nd row합계
3rd row합계
4th row합계
5th row성내동

Common Values

ValueCountFrequency (%)
합계 4
 
2.6%
성내동 4
 
2.6%
대소원면 4
 
2.6%
가주동 4
 
2.6%
성남동 4
 
2.6%
교현동 4
 
2.6%
용산동 4
 
2.6%
호암동 4
 
2.6%
직동 4
 
2.6%
단월동 4
 
2.6%
Other values (30) 115
74.2%

Length

2023-12-12T11:25:07.662156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합계 4
 
2.6%
목행동 4
 
2.6%
성내동 4
 
2.6%
안림동 4
 
2.6%
지현동 4
 
2.6%
문화동 4
 
2.6%
금릉동 4
 
2.6%
주덕읍 4
 
2.6%
산척면 4
 
2.6%
살미면 4
 
2.6%
Other values (30) 115
74.2%

차종
Categorical

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
승용
40 
승합
40 
화물
40 
특수
35 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row승용
2nd row승합
3rd row화물
4th row특수
5th row승용

Common Values

ValueCountFrequency (%)
승용 40
25.8%
승합 40
25.8%
화물 40
25.8%
특수 35
22.6%

Length

2023-12-12T11:25:07.820697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:25:07.971988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승용 40
25.8%
승합 40
25.8%
화물 40
25.8%
특수 35
22.6%


Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1621.0323
Minimum1
Maximum99194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:08.148579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q130.5
median134
Q3755.5
95-th percentile5600.4
Maximum99194
Range99193
Interquartile range (IQR)725

Descriptive statistics

Standard deviation8324.0473
Coefficient of variation (CV)5.1350288
Kurtosis124.72664
Mean1621.0323
Median Absolute Deviation (MAD)127
Skewness10.740953
Sum251260
Variance69289764
MonotonicityNot monotonic
2023-12-12T11:25:08.309841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
3.2%
7 5
 
3.2%
8 4
 
2.6%
79 2
 
1.3%
12 2
 
1.3%
2 2
 
1.3%
15 2
 
1.3%
16 2
 
1.3%
162 2
 
1.3%
14 2
 
1.3%
Other values (118) 127
81.9%
ValueCountFrequency (%)
1 1
 
0.6%
2 2
 
1.3%
3 5
3.2%
4 2
 
1.3%
5 2
 
1.3%
6 1
 
0.6%
7 5
3.2%
8 4
2.6%
10 1
 
0.6%
11 2
 
1.3%
ValueCountFrequency (%)
99194 1
0.6%
22185 1
0.6%
15628 1
0.6%
10152 1
0.6%
9550 1
0.6%
8082 1
0.6%
6867 1
0.6%
6180 1
0.6%
5352 1
0.6%
4345 1
0.6%

비사업용 소계
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.2452
Minimum0
Maximum93078
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:08.504091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q119.5
median128
Q3707
95-th percentile4927.4
Maximum93078
Range93078
Interquartile range (IQR)687.5

Descriptive statistics

Standard deviation7805.5291
Coefficient of variation (CV)5.1683854
Kurtosis125.12666
Mean1510.2452
Median Absolute Deviation (MAD)122
Skewness10.764884
Sum234088
Variance60926284
MonotonicityNot monotonic
2023-12-12T11:25:08.670293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
3.9%
3 4
 
2.6%
11 4
 
2.6%
8 3
 
1.9%
6 3
 
1.9%
14 3
 
1.9%
7 3
 
1.9%
12 2
 
1.3%
86 2
 
1.3%
145 2
 
1.3%
Other values (115) 123
79.4%
ValueCountFrequency (%)
0 1
 
0.6%
2 6
3.9%
3 4
2.6%
4 2
 
1.3%
5 2
 
1.3%
6 3
1.9%
7 3
1.9%
8 3
1.9%
9 1
 
0.6%
10 1
 
0.6%
ValueCountFrequency (%)
93078 1
0.6%
20408 1
0.6%
15477 1
0.6%
10090 1
0.6%
7954 1
0.6%
6856 1
0.6%
6096 1
0.6%
5276 1
0.6%
4778 1
0.6%
4318 1
0.6%

비사업용 경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.29677
Minimum0
Maximum11825
Zeros46
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:08.847002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q318
95-th percentile561.3
Maximum11825
Range11825
Interquartile range (IQR)18

Descriptive statistics

Standard deviation975.49045
Coefficient of variation (CV)6.1624152
Kurtosis135.18158
Mean158.29677
Median Absolute Deviation (MAD)4
Skewness11.31792
Sum24536
Variance951581.63
MonotonicityNot monotonic
2023-12-12T11:25:09.048551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
29.7%
1 11
 
7.1%
2 11
 
7.1%
3 8
 
5.2%
15 4
 
2.6%
7 4
 
2.6%
8 4
 
2.6%
4 4
 
2.6%
11 4
 
2.6%
6 4
 
2.6%
Other values (43) 55
35.5%
ValueCountFrequency (%)
0 46
29.7%
1 11
 
7.1%
2 11
 
7.1%
3 8
 
5.2%
4 4
 
2.6%
5 3
 
1.9%
6 4
 
2.6%
7 4
 
2.6%
8 4
 
2.6%
9 1
 
0.6%
ValueCountFrequency (%)
11825 1
0.6%
1973 1
0.6%
1340 1
0.6%
1122 1
0.6%
863 1
0.6%
828 1
0.6%
668 1
0.6%
590 1
0.6%
549 1
0.6%
450 1
0.6%

비사업용 소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.5871
Minimum0
Maximum17336
Zeros13
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:09.225568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median10
Q369
95-th percentile823.2
Maximum17336
Range17336
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation1410.0618
Coefficient of variation (CV)5.7887378
Kurtosis142.72123
Mean243.5871
Median Absolute Deviation (MAD)10
Skewness11.729259
Sum37756
Variance1988274.4
MonotonicityNot monotonic
2023-12-12T11:25:09.725868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
9.7%
0 13
 
8.4%
2 11
 
7.1%
3 11
 
7.1%
10 9
 
5.8%
5 6
 
3.9%
8 5
 
3.2%
4 4
 
2.6%
20 3
 
1.9%
35 3
 
1.9%
Other values (65) 75
48.4%
ValueCountFrequency (%)
0 13
8.4%
1 15
9.7%
2 11
7.1%
3 11
7.1%
4 4
 
2.6%
5 6
 
3.9%
6 2
 
1.3%
7 1
 
0.6%
8 5
 
3.2%
9 2
 
1.3%
ValueCountFrequency (%)
17336 1
0.6%
1653 1
0.6%
1367 1
0.6%
1111 1
0.6%
947 1
0.6%
925 1
0.6%
867 1
0.6%
840 1
0.6%
816 1
0.6%
712 1
0.6%

비사업용 중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean769.75484
Minimum0
Maximum55036
Zeros16
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:09.893986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median53
Q3140.5
95-th percentile2764.8
Maximum55036
Range55036
Interquartile range (IQR)135.5

Descriptive statistics

Standard deviation4536.2797
Coefficient of variation (CV)5.8931487
Kurtosis135.30807
Mean769.75484
Median Absolute Deviation (MAD)51
Skewness11.32198
Sum119312
Variance20577834
MonotonicityNot monotonic
2023-12-12T11:25:10.040233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
10.3%
1 8
 
5.2%
3 6
 
3.9%
6 4
 
2.6%
8 4
 
2.6%
2 4
 
2.6%
54 3
 
1.9%
66 3
 
1.9%
4 3
 
1.9%
5 3
 
1.9%
Other values (90) 101
65.2%
ValueCountFrequency (%)
0 16
10.3%
1 8
5.2%
2 4
 
2.6%
3 6
 
3.9%
4 3
 
1.9%
5 3
 
1.9%
6 4
 
2.6%
7 3
 
1.9%
8 4
 
2.6%
9 1
 
0.6%
ValueCountFrequency (%)
55036 1
0.6%
9141 1
0.6%
6097 1
0.6%
4745 1
0.6%
4007 1
0.6%
3564 1
0.6%
3153 1
0.6%
2839 1
0.6%
2733 1
0.6%
2502 1
0.6%

비사업용 대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.60645
Minimum0
Maximum25106
Zeros32
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:10.208096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q355.5
95-th percentile1247.6
Maximum25106
Range25106
Interquartile range (IQR)54.5

Descriptive statistics

Standard deviation2067.9502
Coefficient of variation (CV)6.107238
Kurtosis135.95982
Mean338.60645
Median Absolute Deviation (MAD)7
Skewness11.360743
Sum52484
Variance4276418
MonotonicityNot monotonic
2023-12-12T11:25:10.360304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
20.6%
1 18
 
11.6%
2 9
 
5.8%
3 9
 
5.8%
5 4
 
2.6%
16 3
 
1.9%
10 3
 
1.9%
7 3
 
1.9%
27 2
 
1.3%
4 2
 
1.3%
Other values (60) 70
45.2%
ValueCountFrequency (%)
0 32
20.6%
1 18
11.6%
2 9
 
5.8%
3 9
 
5.8%
4 2
 
1.3%
5 4
 
2.6%
6 2
 
1.3%
7 3
 
1.9%
8 2
 
1.3%
10 3
 
1.9%
ValueCountFrequency (%)
25106 1
0.6%
4210 1
0.6%
2581 1
0.6%
1970 1
0.6%
1967 1
0.6%
1600 1
0.6%
1398 1
0.6%
1277 1
0.6%
1235 1
0.6%
1089 1
0.6%

사업용 소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.7871
Minimum0
Maximum6116
Zeros43
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:10.521680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q326.5
95-th percentile184.7
Maximum6116
Range6116
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation635.62782
Coefficient of variation (CV)5.7373813
Kurtosis70.575654
Mean110.7871
Median Absolute Deviation (MAD)4
Skewness8.2580795
Sum17172
Variance404022.73
MonotonicityNot monotonic
2023-12-12T11:25:10.692176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
27.7%
1 19
 
12.3%
4 7
 
4.5%
5 5
 
3.2%
3 5
 
3.2%
2 5
 
3.2%
6 5
 
3.2%
8 4
 
2.6%
23 3
 
1.9%
19 3
 
1.9%
Other values (48) 56
36.1%
ValueCountFrequency (%)
0 43
27.7%
1 19
12.3%
2 5
 
3.2%
3 5
 
3.2%
4 7
 
4.5%
5 5
 
3.2%
6 5
 
3.2%
7 2
 
1.3%
8 4
 
2.6%
9 2
 
1.3%
ValueCountFrequency (%)
6116 1
0.6%
4772 1
0.6%
1777 1
0.6%
408 1
0.6%
404 1
0.6%
287 1
0.6%
285 1
0.6%
198 1
0.6%
179 1
0.6%
164 1
0.6%

사업용 경형
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.974194
Minimum0
Maximum1082
Zeros146
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:10.847159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1082
Range1082
Interquartile range (IQR)0

Descriptive statistics

Standard deviation121.65098
Coefficient of variation (CV)8.7054024
Kurtosis74.956788
Mean13.974194
Median Absolute Deviation (MAD)0
Skewness8.716795
Sum2166
Variance14798.96
MonotonicityNot monotonic
2023-12-12T11:25:10.993955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 146
94.2%
1 5
 
3.2%
1082 1
 
0.6%
9 1
 
0.6%
3 1
 
0.6%
1067 1
 
0.6%
ValueCountFrequency (%)
0 146
94.2%
1 5
 
3.2%
3 1
 
0.6%
9 1
 
0.6%
1067 1
 
0.6%
1082 1
 
0.6%
ValueCountFrequency (%)
1082 1
 
0.6%
1067 1
 
0.6%
9 1
 
0.6%
3 1
 
0.6%
1 5
 
3.2%
0 146
94.2%

사업용 소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9096774
Minimum0
Maximum552
Zeros108
Zeros (%)69.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:11.116630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile29.2
Maximum552
Range552
Interquartile range (IQR)2

Descriptive statistics

Standard deviation45.605646
Coefficient of variation (CV)5.7658035
Kurtosis133.78494
Mean7.9096774
Median Absolute Deviation (MAD)0
Skewness11.223377
Sum1226
Variance2079.8749
MonotonicityNot monotonic
2023-12-12T11:25:11.288848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 108
69.7%
2 7
 
4.5%
1 5
 
3.2%
5 4
 
2.6%
8 4
 
2.6%
6 4
 
2.6%
4 2
 
1.3%
3 2
 
1.3%
25 2
 
1.3%
12 2
 
1.3%
Other values (14) 15
 
9.7%
ValueCountFrequency (%)
0 108
69.7%
1 5
 
3.2%
2 7
 
4.5%
3 2
 
1.3%
4 2
 
1.3%
5 4
 
2.6%
6 4
 
2.6%
7 1
 
0.6%
8 4
 
2.6%
9 1
 
0.6%
ValueCountFrequency (%)
552 1
0.6%
71 1
0.6%
66 1
0.6%
61 1
0.6%
52 1
0.6%
47 1
0.6%
41 1
0.6%
32 1
0.6%
28 1
0.6%
25 2
1.3%

사업용 중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.612903
Minimum0
Maximum3036
Zeros62
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:11.469749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile80.5
Maximum3036
Range3036
Interquartile range (IQR)6

Descriptive statistics

Standard deviation291.69278
Coefficient of variation (CV)6.394962
Kurtosis85.04531
Mean45.612903
Median Absolute Deviation (MAD)1
Skewness9.0598805
Sum7070
Variance85084.68
MonotonicityNot monotonic
2023-12-12T11:25:11.657726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 62
40.0%
1 18
 
11.6%
3 12
 
7.7%
2 10
 
6.5%
4 7
 
4.5%
6 6
 
3.9%
11 3
 
1.9%
9 3
 
1.9%
8 2
 
1.3%
34 2
 
1.3%
Other values (27) 30
19.4%
ValueCountFrequency (%)
0 62
40.0%
1 18
 
11.6%
2 10
 
6.5%
3 12
 
7.7%
4 7
 
4.5%
5 2
 
1.3%
6 6
 
3.9%
7 1
 
0.6%
8 2
 
1.3%
9 3
 
1.9%
ValueCountFrequency (%)
3036 1
0.6%
1997 1
0.6%
286 1
0.6%
204 1
0.6%
140 1
0.6%
113 1
0.6%
107 1
0.6%
98 1
0.6%
73 1
0.6%
72 1
0.6%

사업용 대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.290323
Minimum0
Maximum1998
Zeros61
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T11:25:11.834168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile87.4
Maximum1998
Range1998
Interquartile range (IQR)9

Descriptive statistics

Standard deviation224.3871
Coefficient of variation (CV)5.1833086
Kurtosis58.002377
Mean43.290323
Median Absolute Deviation (MAD)1
Skewness7.4650593
Sum6710
Variance50349.571
MonotonicityNot monotonic
2023-12-12T11:25:12.023066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 61
39.4%
1 20
 
12.9%
3 9
 
5.8%
2 9
 
5.8%
5 6
 
3.9%
6 4
 
2.6%
15 3
 
1.9%
26 3
 
1.9%
8 3
 
1.9%
4 3
 
1.9%
Other values (28) 34
21.9%
ValueCountFrequency (%)
0 61
39.4%
1 20
 
12.9%
2 9
 
5.8%
3 9
 
5.8%
4 3
 
1.9%
5 6
 
3.9%
6 4
 
2.6%
7 1
 
0.6%
8 3
 
1.9%
10 1
 
0.6%
ValueCountFrequency (%)
1998 1
0.6%
1708 1
0.6%
938 1
0.6%
305 1
0.6%
268 1
0.6%
151 1
0.6%
119 1
0.6%
93 1
0.6%
85 1
0.6%
76 1
0.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-09-30 00:00:00
Maximum2023-09-30 00:00:00
2023-12-12T11:25:12.138212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:12.231347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T11:25:05.963186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:53.944139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.126627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.240442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.629849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.775523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.992672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.335768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.459495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.521295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.970852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.054577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.046614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.219259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.340997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.731646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.895025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.149414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.445045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.556612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.610870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.075621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.141903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.138716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.306865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.425238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.817999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.005951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.250318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.528592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.651064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.700480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.167585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.236228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.234189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.413963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.507256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.917357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.098867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.386064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.629673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.752099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.789990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.266915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.348998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.342688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.519305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.596783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.042441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.201303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.530212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.749852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.849079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.210177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.370887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.430087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.458863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.601685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.686687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.150170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.309647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.653351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.853686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.940231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.300957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.450451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.514532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.557296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.701998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.058132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.235040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.430546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.784279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.977846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.028045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.417730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.528563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.628439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.667171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.802797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.159786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.342450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.561315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:00.901302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.084456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.125196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.538510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.614600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.749954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.795705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.925940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.269708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.458471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.647817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.009734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.169598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.221086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.645041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.697324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.863533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:54.917902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.017028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.357455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.558431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.764037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.125156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.265074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.321228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.739111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.779861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:06.966880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:55.021534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:56.129272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:57.474969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:58.655385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:59.869817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:01.222706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:02.365393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:03.420482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:04.848512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:25:05.862605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:25:12.314449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동차종비사업용 소계비사업용 경형비사업용 소형비사업용 중형비사업용 대형사업용 소계사업용 경형사업용 소형사업용 중형사업용 대형
읍면동1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
차종0.0001.0000.1910.1910.1020.0000.1020.1020.0000.2050.0500.0000.000
0.0000.1911.0001.0001.0001.0001.0001.0000.9810.8890.6710.6710.838
비사업용 소계0.0000.1911.0001.0001.0001.0001.0001.0000.9810.8890.6710.6710.838
비사업용 경형0.0000.1021.0001.0001.0000.0001.0001.0000.6710.4520.0000.9410.711
비사업용 소형0.0000.0001.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.000
비사업용 중형0.0000.1021.0001.0001.0000.0001.0001.0000.6710.4520.0000.9410.711
비사업용 대형0.0000.1021.0001.0001.0000.0001.0001.0000.6710.4520.0000.9410.711
사업용 소계0.0000.0000.9810.9810.6711.0000.6710.6711.0001.0000.6711.0001.000
사업용 경형0.0000.2050.8890.8890.4520.0000.4520.4521.0001.0000.0001.0001.000
사업용 소형0.0000.0500.6710.6710.0001.0000.0000.0000.6710.0001.0000.0000.711
사업용 중형0.0000.0000.6710.6710.9410.0000.9410.9411.0001.0000.0001.0001.000
사업용 대형0.0000.0000.8380.8380.7111.0000.7110.7111.0001.0000.7111.0001.000
2023-12-12T11:25:12.474002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차종읍면동
차종1.0000.000
읍면동0.0001.000
2023-12-12T11:25:12.585947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비사업용 소계비사업용 경형비사업용 소형비사업용 중형비사업용 대형사업용 소계사업용 경형사업용 소형사업용 중형사업용 대형읍면동차종
1.0000.9940.8860.6790.8850.9180.6930.3800.2380.6840.6060.0000.075
비사업용 소계0.9941.0000.9000.6690.8940.9220.6390.3790.2000.6360.5520.0000.075
비사업용 경형0.8860.9001.0000.3680.9400.8800.4370.358-0.0820.5110.3470.0000.095
비사업용 소형0.6790.6690.3681.0000.3050.5410.7110.2770.6430.5610.6670.0000.000
비사업용 중형0.8850.8940.9400.3051.0000.8590.4370.355-0.1230.5240.3520.0000.095
비사업용 대형0.9180.9220.8800.5410.8591.0000.5820.3700.0950.5850.4920.0000.095
사업용 소계0.6930.6390.4370.7110.4370.5821.0000.3810.5300.9060.9290.0000.000
사업용 경형0.3800.3790.3580.2770.3550.3700.3811.000-0.0170.3950.3760.0000.135
사업용 소형0.2380.200-0.0820.643-0.1230.0950.530-0.0171.0000.3140.5250.0000.045
사업용 중형0.6840.6360.5110.5610.5240.5850.9060.3950.3141.0000.7810.0000.000
사업용 대형0.6060.5520.3470.6670.3520.4920.9290.3760.5250.7811.0000.0000.000
읍면동0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
차종0.0750.0750.0950.0000.0950.0950.0000.1350.0450.0000.0000.0001.000

Missing values

2023-12-12T11:25:07.118694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:25:07.325457image/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

통계기준년월읍면동차종비사업용 소계비사업용 경형비사업용 소형비사업용 중형비사업용 대형사업용 소계사업용 경형사업용 소형사업용 중형사업용 대형데이터기준일자
02023-09합계승용99194930781182511115503625106611610820303619982023-09-30
12023-09합계승합35393131186972733115408001402682023-09-30
22023-09합계화물2218520408256173361829987177715522869382023-09-30
32023-09합계특수71242713345834285061731512023-09-30
42023-09성내동승용1701691969945100012023-09-30
52023-09성내동승합111101100000002023-09-30
62023-09성내동화물504523580502212023-09-30
72023-09성내동특수320110100012023-09-30
82023-09성남동승용27927835917757100102023-09-30
92023-09성남동승합10101270000002023-09-30
통계기준년월읍면동차종비사업용 소계비사업용 경형비사업용 소형비사업용 중형비사업용 대형사업용 소계사업용 경형사업용 소형사업용 중형사업용 대형데이터기준일자
1452023-09산척면화물48847984103823902252023-09-30
1462023-09산척면특수320200100012023-09-30
1472023-09엄정면승용119011671462066733423002032023-09-30
1482023-09엄정면승합555541491000002023-09-30
1492023-09엄정면화물83465565814523179024361192023-09-30
1502023-09엄정면특수1690711701062023-09-30
1512023-09소태면승용7187139010408205500232023-09-30
1522023-09소태면승합373732320000002023-09-30
1532023-09소태면화물46946284102816702142023-09-30
1542023-09소태면특수760501100102023-09-30