Overview

Dataset statistics

Number of variables13
Number of observations61
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory115.2 B

Variable types

Numeric6
Categorical6
Text1

Dataset

Description대전광역시 유성구 인구수 대비 동별 교통사고 비율 상세정보에 대한 데이터로 행정동이름, 교통사고피해자수, 비율 등의 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15108903/fileData.do

Alerts

기준년도 has constant value ""Constant
시도코드 has constant value ""Constant
시도이름 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구이름 has constant value ""Constant
번호 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
인구수 is highly overall correlated with 교통사고피해자수High correlation
교통사고피해자수 is highly overall correlated with 인구수High correlation
행정동이름 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
번호 has unique valuesUnique
교통사고피해자수 has 6 (9.8%) zerosZeros
비율(퍼센트) has 6 (9.8%) zerosZeros

Reproduction

Analysis started2023-12-12 23:47:34.026372
Analysis finished2023-12-12 23:47:37.284530
Duration3.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:47:37.357662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median31
Q346
95-th percentile58
Maximum61
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.752934
Coefficient of variation (CV)0.57267529
Kurtosis-1.2
Mean31
Median Absolute Deviation (MAD)15
Skewness0
Sum1891
Variance315.16667
MonotonicityNot monotonic
2023-12-13T08:47:37.490726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
8 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
44 1
 
1.6%
45 1
 
1.6%
46 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
54 1
1.6%
53 1
1.6%
52 1
1.6%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
2021
61 

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 61
100.0%

Length

2023-12-13T08:47:37.585599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:47:37.654693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 61
100.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
3000000000
61 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000000
2nd row3000000000
3rd row3000000000
4th row3000000000
5th row3000000000

Common Values

ValueCountFrequency (%)
3000000000 61
100.0%

Length

2023-12-13T08:47:37.727507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:47:37.797885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000000 61
100.0%

시도이름
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
대전광역시
61 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 61
100.0%

Length

2023-12-13T08:47:37.872748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:47:37.941254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 61
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
3020000000
61 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000000
2nd row3020000000
3rd row3020000000
4th row3020000000
5th row3020000000

Common Values

ValueCountFrequency (%)
3020000000 61
100.0%

Length

2023-12-13T08:47:38.013499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:47:38.082578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000 61
100.0%

시군구이름
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
유성구
61 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 61
100.0%

Length

2023-12-13T08:47:38.156066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:47:38.231684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 61
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200551 × 109
Minimum3.020052 × 109
Maximum3.020061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:47:38.306297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.020052 × 109
5-th percentile3.020052 × 109
Q13.020053 × 109
median3.0200547 × 109
Q33.020057 × 109
95-th percentile3.02006 × 109
Maximum3.020061 × 109
Range9000
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2416.7381
Coefficient of variation (CV)8.002298 × 10-7
Kurtosis-0.03371248
Mean3.0200551 × 109
Median Absolute Deviation (MAD)1700
Skewness0.77107207
Sum1.8422336 × 1011
Variance5840623
MonotonicityNot monotonic
2023-12-13T08:47:38.398510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3020055000 11
18.0%
3020058000 8
13.1%
3020052000 8
13.1%
3020054700 7
11.5%
3020054000 5
8.2%
3020052600 4
 
6.6%
3020054600 4
 
6.6%
3020060000 3
 
4.9%
3020057000 3
 
4.9%
3020052700 2
 
3.3%
Other values (3) 6
9.8%
ValueCountFrequency (%)
3020052000 8
13.1%
3020052600 4
 
6.6%
3020052700 2
 
3.3%
3020053000 2
 
3.3%
3020054000 5
8.2%
3020054600 4
 
6.6%
3020054700 7
11.5%
3020054800 2
 
3.3%
3020055000 11
18.0%
3020057000 3
 
4.9%
ValueCountFrequency (%)
3020061000 2
 
3.3%
3020060000 3
 
4.9%
3020058000 8
13.1%
3020057000 3
 
4.9%
3020055000 11
18.0%
3020054800 2
 
3.3%
3020054700 7
11.5%
3020054600 4
 
6.6%
3020054000 5
8.2%
3020053000 2
 
3.3%

행정동이름
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
신성동
11 
구즉동
8 
진잠동
8 
노은2동
7 
온천2동
5 
Other values (8)
22 

Length

Max length4
Median length3
Mean length3.3606557
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관평동
2nd row관평동
3rd row관평동
4th row구즉동
5th row구즉동

Common Values

ValueCountFrequency (%)
신성동 11
18.0%
구즉동 8
13.1%
진잠동 8
13.1%
노은2동 7
11.5%
온천2동 5
8.2%
학하동 4
 
6.6%
노은1동 4
 
6.6%
관평동 3
 
4.9%
전민동 3
 
4.9%
상대동 2
 
3.3%
Other values (3) 6
9.8%

Length

2023-12-13T08:47:38.486495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신성동 11
18.0%
구즉동 8
13.1%
진잠동 8
13.1%
노은2동 7
11.5%
온천2동 5
8.2%
학하동 4
 
6.6%
노은1동 4
 
6.6%
관평동 3
 
4.9%
전민동 3
 
4.9%
상대동 2
 
3.3%
Other values (3) 6
9.8%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200126 × 109
Minimum3.0200101 × 109
Maximum3.0200153 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:47:38.595225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200101 × 109
5-th percentile3.0200104 × 109
Q13.0200115 × 109
median3.0200125 × 109
Q33.0200139 × 109
95-th percentile3.020015 × 109
Maximum3.0200153 × 109
Range5200
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1477.4276
Coefficient of variation (CV)4.8921239 × 10-7
Kurtosis-1.0890794
Mean3.0200126 × 109
Median Absolute Deviation (MAD)1200
Skewness0.09889468
Sum1.8422077 × 1011
Variance2182792.3
MonotonicityNot monotonic
2023-12-13T08:47:38.730523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3020012100 3
 
4.9%
3020012000 3
 
4.9%
3020011100 2
 
3.3%
3020011600 2
 
3.3%
3020013200 2
 
3.3%
3020013900 2
 
3.3%
3020014600 1
 
1.6%
3020010100 1
 
1.6%
3020010600 1
 
1.6%
3020011300 1
 
1.6%
Other values (43) 43
70.5%
ValueCountFrequency (%)
3020010100 1
1.6%
3020010200 1
1.6%
3020010300 1
1.6%
3020010400 1
1.6%
3020010500 1
1.6%
3020010600 1
1.6%
3020010700 1
1.6%
3020010800 1
1.6%
3020010900 1
1.6%
3020011000 1
1.6%
ValueCountFrequency (%)
3020015300 1
1.6%
3020015200 1
1.6%
3020015100 1
1.6%
3020015000 1
1.6%
3020014900 1
1.6%
3020014800 1
1.6%
3020014700 1
1.6%
3020014600 1
1.6%
3020014500 1
1.6%
3020014400 1
1.6%

법정동이름
Text

Distinct53
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-13T08:47:38.916237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.852459
Min length2

Characters and Unicode

Total characters174
Distinct characters66
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

Unique47 ?
Unique (%)77.0%

Sample

1st row관평동
2nd row용산동
3rd row탑립동
4th row신동
5th row구룡동
ValueCountFrequency (%)
지족동 3
 
4.9%
죽동 3
 
4.9%
반석동 2
 
3.3%
봉명동 2
 
3.3%
하기동 2
 
3.3%
복용동 2
 
3.3%
노은동 1
 
1.6%
관평동 1
 
1.6%
구암동 1
 
1.6%
용계동 1
 
1.6%
Other values (43) 43
70.5%
2023-12-13T08:47:39.205005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
대 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
원 3
 
1.7%
족 3
 
1.7%
정 3
 
1.7%
Other values (56) 80
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 174
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
대 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
원 3
 
1.7%
족 3
 
1.7%
정 3
 
1.7%
Other values (56) 80
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 174
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
대 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
원 3
 
1.7%
족 3
 
1.7%
정 3
 
1.7%
Other values (56) 80
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 174
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
대 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
원 3
 
1.7%
족 3
 
1.7%
정 3
 
1.7%
Other values (56) 80
46.0%

인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5741.1148
Minimum4
Maximum24453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:47:39.321465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile31
Q1354
median1547
Q310495
95-th percentile22168
Maximum24453
Range24449
Interquartile range (IQR)10141

Descriptive statistics

Standard deviation6993.7262
Coefficient of variation (CV)1.2181826
Kurtosis0.48357653
Mean5741.1148
Median Absolute Deviation (MAD)1542
Skewness1.1796656
Sum350208
Variance48912206
MonotonicityNot monotonic
2023-12-13T08:47:39.431483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2
 
3.3%
31 2
 
3.3%
23429 1
 
1.6%
138 1
 
1.6%
1061 1
 
1.6%
12168 1
 
1.6%
15152 1
 
1.6%
408 1
 
1.6%
14883 1
 
1.6%
6141 1
 
1.6%
Other values (49) 49
80.3%
ValueCountFrequency (%)
4 2
3.3%
5 1
1.6%
31 2
3.3%
42 1
1.6%
110 1
1.6%
118 1
1.6%
124 1
1.6%
128 1
1.6%
138 1
1.6%
150 1
1.6%
ValueCountFrequency (%)
24453 1
1.6%
23429 1
1.6%
23154 1
1.6%
22168 1
1.6%
18367 1
1.6%
15327 1
1.6%
15152 1
1.6%
14883 1
1.6%
12669 1
1.6%
12481 1
1.6%

교통사고피해자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.229508
Minimum0
Maximum278
Zeros6
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:47:39.555247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median13
Q334
95-th percentile97
Maximum278
Range278
Interquartile range (IQR)30

Descriptive statistics

Standard deviation47.820983
Coefficient of variation (CV)1.5312756
Kurtosis13.53516
Mean31.229508
Median Absolute Deviation (MAD)12
Skewness3.3191292
Sum1905
Variance2286.8464
MonotonicityNot monotonic
2023-12-13T08:47:39.658900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 6
 
9.8%
3 4
 
6.6%
2 4
 
6.6%
12 4
 
6.6%
34 2
 
3.3%
26 2
 
3.3%
51 2
 
3.3%
9 2
 
3.3%
11 2
 
3.3%
6 2
 
3.3%
Other values (30) 31
50.8%
ValueCountFrequency (%)
0 6
9.8%
1 1
 
1.6%
2 4
6.6%
3 4
6.6%
4 2
 
3.3%
5 1
 
1.6%
6 2
 
3.3%
7 1
 
1.6%
8 1
 
1.6%
9 2
 
3.3%
ValueCountFrequency (%)
278 1
1.6%
206 1
1.6%
107 1
1.6%
97 1
1.6%
89 1
1.6%
84 1
1.6%
83 1
1.6%
68 1
1.6%
55 1
1.6%
54 1
1.6%

비율(퍼센트)
Real number (ℝ)

ZEROS 

Distinct50
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5429508
Minimum0
Maximum150
Zeros6
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:47:39.981800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median0.59
Q32.42
95-th percentile25
Maximum150
Range150
Interquartile range (IQR)2.17

Descriptive statistics

Standard deviation20.076314
Coefficient of variation (CV)3.6219542
Kurtosis46.419061
Mean5.5429508
Median Absolute Deviation (MAD)0.54
Skewness6.5299345
Sum338.12
Variance403.05839
MonotonicityNot monotonic
2023-12-13T08:47:40.086863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
9.8%
0.27 3
 
4.9%
0.36 2
 
3.3%
0.14 2
 
3.3%
0.12 2
 
3.3%
0.43 2
 
3.3%
0.96 1
 
1.6%
0.09 1
 
1.6%
0.58 1
 
1.6%
1.3 1
 
1.6%
Other values (40) 40
65.6%
ValueCountFrequency (%)
0.0 6
9.8%
0.07 1
 
1.6%
0.09 1
 
1.6%
0.1 1
 
1.6%
0.12 2
 
3.3%
0.14 2
 
3.3%
0.18 1
 
1.6%
0.22 1
 
1.6%
0.25 1
 
1.6%
0.27 3
4.9%
ValueCountFrequency (%)
150.0 1
1.6%
40.0 1
1.6%
25.81 1
1.6%
25.0 1
1.6%
14.66 1
1.6%
13.42 1
1.6%
7.63 1
1.6%
7.14 1
1.6%
7.03 1
1.6%
6.45 1
1.6%

Interactions

2023-12-13T08:47:36.649243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.357535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.808777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.429143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.838985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.238578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.716419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.444025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.882413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.502558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.901564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.310200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.776760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.521448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.939880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.562914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.963164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.386328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.847993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.603984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.231755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.625037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.040238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.448871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.913997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.669138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.297619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.694670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.098883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.508031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.989542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:34.729927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.358425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:35.771426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.164320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:47:36.584563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:47:40.166239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드행정동이름법정동코드법정동이름인구수교통사고피해자수비율(퍼센트)
번호1.0000.8830.9130.8440.0000.2980.0000.072
행정동코드0.8831.0001.0000.8610.8990.2630.4830.000
행정동이름0.9131.0001.0000.8760.0000.4790.4970.000
법정동코드0.8440.8610.8761.0001.0000.0000.0000.418
법정동이름0.0000.8990.0001.0001.0000.4190.6081.000
인구수0.2980.2630.4790.0000.4191.0000.7480.000
교통사고피해자수0.0000.4830.4970.0000.6080.7481.0000.000
비율(퍼센트)0.0720.0000.0000.4181.0000.0000.0001.000
2023-12-13T08:47:40.262880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드법정동코드인구수교통사고피해자수비율(퍼센트)행정동이름
번호1.000-0.522-0.4580.3390.233-0.1650.664
행정동코드-0.5221.0000.810-0.107-0.1090.0380.952
법정동코드-0.4580.8101.000-0.315-0.3140.0820.601
인구수0.339-0.107-0.3151.0000.769-0.4170.211
교통사고피해자수0.233-0.109-0.3140.7691.0000.1510.248
비율(퍼센트)-0.1650.0380.082-0.4170.1511.0000.000
행정동이름0.6640.9520.6010.2110.2480.0001.000

Missing values

2023-12-13T08:47:37.100501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:47:37.227930image/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

번호기준년도시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름인구수교통사고피해자수비율(퍼센트)
0120213000000000대전광역시3020000000유성구3020060000관평동3020014600관평동23429840.36
1220213000000000대전광역시3020000000유성구3020060000관평동3020014400용산동4588270.59
2320213000000000대전광역시3020000000유성구3020060000관평동3020014300탑립동498142.81
3520213000000000대전광역시3020000000유성구3020058000구즉동3020015100신동4125.0
4620213000000000대전광역시3020000000유성구3020058000구즉동3020015300구룡동12432.42
5720213000000000대전광역시3020000000유성구3020058000구즉동3020014800금고동5240.0
6920213000000000대전광역시3020000000유성구3020058000구즉동3020015200둔곡동31825.81
71020213000000000대전광역시3020000000유성구3020058000구즉동3020014500봉산동8091310.38
81120213000000000대전광역시3020000000유성구3020058000구즉동3020014700송강동18367500.27
91220213000000000대전광역시3020000000유성구3020052700상대동3020011600복용동45730.66
번호기준년도시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름인구수교통사고피해자수비율(퍼센트)
515320213000000000대전광역시3020000000유성구3020054700노은2동3020012000지족동11860510.43
525420213000000000대전광역시3020000000유성구3020054700노은2동3020013200하기동10495150.14
535520213000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동6040971.61
545620213000000000대전광역시3020000000유성구3020054000온천2동3020012100죽동10389120.12
555720213000000000대전광역시3020000000유성구3020054000온천2동3020012400구성동585193.25
565820213000000000대전광역시3020000000유성구3020054000온천2동3020012300어은동10032120.12
575920213000000000대전광역시3020000000유성구3020054000온천2동3020011700장대동124812061.65
586020213000000000대전광역시3020000000유성구3020054800노은3동3020013900반석동12669550.43
596120213000000000대전광역시3020000000유성구3020054800노은3동3020012000지족동24453240.1
60420213000000000대전광역시3020000000유성구3020058000구즉동3020014900대동31900.0