Overview

Dataset statistics

Number of variables7
Number of observations1130
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.4 KiB
Average record size in memory61.1 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 교통문화지수(점), 운전행태영역(점), 교통안전영역(점), 보행행태영역(점)으로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15110162/fileData.do

Alerts

교통문화지수(점) is highly overall correlated with 운전행태영역(점) and 1 other fieldsHigh correlation
운전행태영역(점) is highly overall correlated with 교통문화지수(점)High correlation
교통안전영역(점) is highly overall correlated with 교통문화지수(점)High correlation

Reproduction

Analysis started2023-12-12 10:19:16.351508
Analysis finished2023-12-12 10:19:18.823533
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2018
228 
2017
226 
2019
226 
2020
225 
2021
225 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2018 228
20.2%
2017 226
20.0%
2019 226
20.0%
2020 225
19.9%
2021 225
19.9%

Length

2023-12-12T19:19:18.891575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:19:19.017379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 228
20.2%
2017 226
20.0%
2019 226
20.0%
2020 225
19.9%
2021 225
19.9%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
155 
서울특별시
125 
경상북도
111 
전라남도
106 
강원도
90 
Other values (11)
543 

Length

Max length7
Median length5
Mean length4.1353982
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.7%
서울특별시 125
11.1%
경상북도 111
9.8%
전라남도 106
9.4%
강원도 90
8.0%
경상남도 90
8.0%
부산광역시 80
7.1%
충청남도 75
6.6%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (6) 173
15.3%

Length

2023-12-12T19:19:19.179400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.7%
서울특별시 125
11.1%
경상북도 111
9.8%
전라남도 106
9.4%
강원도 90
8.0%
경상남도 90
8.0%
부산광역시 80
7.1%
충청남도 75
6.6%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (6) 173
15.3%

시군구명
Text

Distinct206
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-12T19:19:19.563373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9230088
Min length2

Characters and Unicode

Total characters3303
Distinct characters132
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

Unique2 ?
Unique (%)0.2%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 30
 
2.7%
동구 30
 
2.7%
서구 25
 
2.2%
남구 21
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
장수군 5
 
0.4%
곡성군 5
 
0.4%
고흥군 5
 
0.4%
Other values (196) 969
85.8%
2023-12-12T19:19:20.441589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 415
 
12.6%
시 390
 
11.8%
구 370
 
11.2%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.6%
산 75
 
2.3%
서 65
 
2.0%
Other values (122) 1513
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3303
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 415
 
12.6%
시 390
 
11.8%
구 370
 
11.2%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.6%
산 75
 
2.3%
서 65
 
2.0%
Other values (122) 1513
45.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3303
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 415
 
12.6%
시 390
 
11.8%
구 370
 
11.2%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.6%
산 75
 
2.3%
서 65
 
2.0%
Other values (122) 1513
45.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3303
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 415
 
12.6%
시 390
 
11.8%
구 370
 
11.2%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.6%
산 75
 
2.3%
서 65
 
2.0%
Other values (122) 1513
45.8%

교통문화지수(점)
Real number (ℝ)

HIGH CORRELATION 

Distinct880
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.445416
Minimum42.78
Maximum92.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T19:19:20.639127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.78
5-th percentile69.0705
Q174.83
median79.16
Q382.35
95-th percentile86.2355
Maximum92.46
Range49.68
Interquartile range (IQR)7.52

Descriptive statistics

Standard deviation5.4412714
Coefficient of variation (CV)0.069363791
Kurtosis2.9120617
Mean78.445416
Median Absolute Deviation (MAD)3.595
Skewness-0.86743174
Sum88643.32
Variance29.607435
MonotonicityNot monotonic
2023-12-12T19:19:20.786018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.51 4
 
0.4%
73.87 4
 
0.4%
83.49 4
 
0.4%
77.46 4
 
0.4%
82.6 4
 
0.4%
73.13 3
 
0.3%
81.08 3
 
0.3%
81.82 3
 
0.3%
80.59 3
 
0.3%
80.29 3
 
0.3%
Other values (870) 1095
96.9%
ValueCountFrequency (%)
42.78 1
0.1%
42.88 1
0.1%
58.76 1
0.1%
59.91 1
0.1%
60.63 1
0.1%
60.76 1
0.1%
61.54 1
0.1%
61.82 1
0.1%
62.66 1
0.1%
63.78 1
0.1%
ValueCountFrequency (%)
92.46 1
0.1%
92.26 1
0.1%
90.68 1
0.1%
89.92 1
0.1%
89.73 1
0.1%
89.56 1
0.1%
89.1 1
0.1%
89.08 1
0.1%
89.0 1
0.1%
88.9 1
0.1%

운전행태영역(점)
Real number (ℝ)

HIGH CORRELATION 

Distinct661
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.881062
Minimum24.92
Maximum53.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T19:19:20.928144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.92
5-th percentile41.7835
Q144.47
median45.955
Q347.525
95-th percentile49.781
Maximum53.56
Range28.64
Interquartile range (IQR)3.055

Descriptive statistics

Standard deviation2.6262165
Coefficient of variation (CV)0.057239662
Kurtosis6.96641
Mean45.881062
Median Absolute Deviation (MAD)1.515
Skewness-1.108861
Sum51845.6
Variance6.897013
MonotonicityNot monotonic
2023-12-12T19:19:21.110056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.97 8
 
0.7%
45.39 7
 
0.6%
45.63 6
 
0.5%
45.34 6
 
0.5%
45.47 6
 
0.5%
46.04 6
 
0.5%
46.3 6
 
0.5%
46.82 6
 
0.5%
46.37 6
 
0.5%
44.47 5
 
0.4%
Other values (651) 1068
94.5%
ValueCountFrequency (%)
24.92 1
0.1%
25.67 1
0.1%
33.7 1
0.1%
36.35 1
0.1%
36.49 1
0.1%
36.66 1
0.1%
36.87 1
0.1%
37.84 1
0.1%
37.9 1
0.1%
38.0 1
0.1%
ValueCountFrequency (%)
53.56 1
0.1%
52.88 1
0.1%
52.78 1
0.1%
52.56 1
0.1%
52.42 1
0.1%
52.11 1
0.1%
52.08 1
0.1%
51.99 1
0.1%
51.82 1
0.1%
51.7 1
0.1%

교통안전영역(점)
Real number (ℝ)

HIGH CORRELATION 

Distinct804
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.708487
Minimum3.98
Maximum27.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T19:19:21.281888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.98
5-th percentile9.089
Q113.6125
median17.205
Q319.6075
95-th percentile24.4055
Maximum27.98
Range24
Interquartile range (IQR)5.995

Descriptive statistics

Standard deviation4.5103339
Coefficient of variation (CV)0.26994269
Kurtosis-0.37393082
Mean16.708487
Median Absolute Deviation (MAD)2.82
Skewness-0.096099162
Sum18880.59
Variance20.343112
MonotonicityNot monotonic
2023-12-12T19:19:21.481833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.04 5
 
0.4%
19.56 5
 
0.4%
16.98 4
 
0.4%
19.38 4
 
0.4%
17.53 4
 
0.4%
16.46 4
 
0.4%
18.24 4
 
0.4%
16.58 4
 
0.4%
13.9 4
 
0.4%
17.32 4
 
0.4%
Other values (794) 1088
96.3%
ValueCountFrequency (%)
3.98 1
0.1%
4.65 1
0.1%
4.94 1
0.1%
6.04 1
0.1%
6.13 1
0.1%
6.42 1
0.1%
6.68 2
0.2%
6.96 1
0.1%
7.08 1
0.1%
7.1 1
0.1%
ValueCountFrequency (%)
27.98 1
0.1%
27.72 1
0.1%
27.25 1
0.1%
27.12 2
0.2%
27.11 1
0.1%
27.1 1
0.1%
27.03 2
0.2%
26.93 1
0.1%
26.89 1
0.1%
26.72 1
0.1%

보행행태영역(점)
Real number (ℝ)

Distinct493
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.855982
Minimum7.75
Maximum19.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T19:19:21.661030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.75
5-th percentile12.489
Q114.7425
median16.44
Q317.13
95-th percentile17.85
Maximum19.06
Range11.31
Interquartile range (IQR)2.3875

Descriptive statistics

Standard deviation1.7964556
Coefficient of variation (CV)0.11329829
Kurtosis1.375268
Mean15.855982
Median Absolute Deviation (MAD)0.85
Skewness-1.2190465
Sum17917.26
Variance3.2272528
MonotonicityNot monotonic
2023-12-12T19:19:21.838403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.81 9
 
0.8%
16.82 9
 
0.8%
16.6 8
 
0.7%
16.54 8
 
0.7%
17.19 8
 
0.7%
17.16 8
 
0.7%
16.86 8
 
0.7%
17.09 7
 
0.6%
17.18 7
 
0.6%
16.4 7
 
0.6%
Other values (483) 1051
93.0%
ValueCountFrequency (%)
7.75 1
0.1%
7.97 1
0.1%
8.44 2
0.2%
8.95 1
0.1%
9.39 1
0.1%
9.57 1
0.1%
9.71 1
0.1%
9.84 1
0.1%
9.92 1
0.1%
10.03 1
0.1%
ValueCountFrequency (%)
19.06 1
0.1%
18.82 1
0.1%
18.63 1
0.1%
18.54 2
0.2%
18.52 1
0.1%
18.49 2
0.2%
18.47 1
0.1%
18.4 1
0.1%
18.37 2
0.2%
18.31 1
0.1%

Interactions

2023-12-12T19:19:18.112930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:16.762238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.190690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.631818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.211583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:16.870424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.292839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.799797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.339450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:16.989049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.403508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.900687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.450828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.097888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.494900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:17.997792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:19:21.961032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명교통문화지수(점)운전행태영역(점)교통안전영역(점)보행행태영역(점)
통계연도1.0000.0000.3850.2540.7500.791
시도명0.0001.0000.3060.3750.3010.085
교통문화지수(점)0.3850.3061.0000.9070.6480.629
운전행태영역(점)0.2540.3750.9071.0000.0940.584
교통안전영역(점)0.7500.3010.6480.0941.0000.577
보행행태영역(점)0.7910.0850.6290.5840.5771.000
2023-12-12T19:19:22.079340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T19:19:22.170034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교통문화지수(점)운전행태영역(점)교통안전영역(점)보행행태영역(점)통계연도시도명
교통문화지수(점)1.0000.5590.8040.1000.2480.112
운전행태영역(점)0.5591.0000.1070.1280.1580.140
교통안전영역(점)0.8040.1071.000-0.2790.4060.121
보행행태영역(점)0.1000.128-0.2791.0000.4460.033
통계연도0.2480.1580.4060.4461.0000.000
시도명0.1120.1400.1210.0330.0001.000

Missing values

2023-12-12T19:19:18.625958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:19:18.769145image/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

통계연도시도명시군구명교통문화지수(점)운전행태영역(점)교통안전영역(점)보행행태영역(점)
02017서울특별시종로구85.6147.9524.1913.47
12017서울특별시중구81.8848.4120.1713.3
22017서울특별시용산구79.4241.724.6813.04
32017서울특별시성동구84.3743.6526.9313.78
42017서울특별시광진구88.1949.0625.0814.05
52017서울특별시동대문구85.2745.1127.0313.12
62017서울특별시중랑구84.4748.1222.8313.52
72017서울특별시성북구86.5646.2726.8913.39
82017서울특별시강북구78.643.1521.9713.49
92017서울특별시도봉구89.0849.125.2114.78
통계연도시도명시군구명교통문화지수(점)운전행태영역(점)교통안전영역(점)보행행태영역(점)
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