Overview

Dataset statistics

Number of variables11
Number of observations142
Missing cells142
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.6 KiB
Average record size in memory97.9 B

Variable types

Numeric8
Categorical1
Text1
Unsupported1

Dataset

Description2018년~ 2021년 5월까지의 불법주정차 상습 발생지에 대한 현장정보로 주소, 민원발생누계, 년도별 민원발생 건수, 현장정보 등의 항목을 제공합니다.
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15096498/fileData.do

Alerts

순번 is highly overall correlated with 민원발생누적건수 and 4 other fieldsHigh correlation
민원발생누적건수 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
2018년 민원발생건수 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
2019년 민원발생건수 is highly overall correlated with 주소High correlation
2020년 민원발생건수 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
2021년 민원발생건수 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 2021년 민원발생건수 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 2018년 민원발생건수 and 1 other fieldsHigh correlation
주소 is highly overall correlated with 순번 and 7 other fieldsHigh correlation
Unnamed: 10 has 142 (100.0%) missing valuesMissing
순번 has unique valuesUnique
현장정보 has unique valuesUnique
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2018년 민원발생건수 has 85 (59.9%) zerosZeros
2019년 민원발생건수 has 84 (59.2%) zerosZeros
2020년 민원발생건수 has 70 (49.3%) zerosZeros
2021년 민원발생건수 has 55 (38.7%) zerosZeros

Reproduction

Analysis started2023-12-11 22:51:23.501857
Analysis finished2023-12-11 22:51:29.906907
Duration6.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.5
Minimum1
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:29.992099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.05
Q136.25
median71.5
Q3106.75
95-th percentile134.95
Maximum142
Range141
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation41.135953
Coefficient of variation (CV)0.57532802
Kurtosis-1.2
Mean71.5
Median Absolute Deviation (MAD)35.5
Skewness0
Sum10153
Variance1692.1667
MonotonicityStrictly increasing
2023-12-12T07:51:30.167904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
99 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
97 1
 
0.7%
98 1
 
0.7%
100 1
 
0.7%
91 1
 
0.7%
Other values (132) 132
93.0%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
142 1
0.7%
141 1
0.7%
140 1
0.7%
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%

주소
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
대구광역시 북구 복현동 623
20 
대구광역시 북구 산격동 1485
15 
대구광역시 북구 노원동3가 252-3
13 
대구광역시 북구 노원동2가 1
 
8
대구광역시 북구 산격동 172-5
 
6
Other values (20)
80 

Length

Max length20
Median length19
Mean length17.309859
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 북구 복현동 623
2nd row대구광역시 북구 복현동 623
3rd row대구광역시 북구 복현동 623
4th row대구광역시 북구 복현동 623
5th row대구광역시 북구 복현동 623

Common Values

ValueCountFrequency (%)
대구광역시 북구 복현동 623 20
 
14.1%
대구광역시 북구 산격동 1485 15
 
10.6%
대구광역시 북구 노원동3가 252-3 13
 
9.2%
대구광역시 북구 노원동2가 1 8
 
5.6%
대구광역시 북구 산격동 172-5 6
 
4.2%
대구광역시 북구 구암동 835-1 6
 
4.2%
대구광역시 북구 산격동 1499-1 6
 
4.2%
대구광역시 북구 서변동 1726 6
 
4.2%
대구광역시 북구 노원동3가 1230 5
 
3.5%
대구광역시 북구 구암동 716 5
 
3.5%
Other values (15) 52
36.6%

Length

2023-12-12T07:51:30.312088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구광역시 142
25.0%
북구 142
25.0%
산격동 32
 
5.6%
복현동 23
 
4.0%
노원동3가 21
 
3.7%
623 20
 
3.5%
1485 15
 
2.6%
구암동 14
 
2.5%
252-3 13
 
2.3%
동천동 11
 
1.9%
Other values (29) 135
23.8%

민원발생누적건수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.48592
Minimum57
Maximum1830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:30.423655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile64
Q1183.25
median330
Q3846
95-th percentile1830
Maximum1830
Range1773
Interquartile range (IQR)662.75

Descriptive statistics

Standard deviation565.97609
Coefficient of variation (CV)0.96174959
Kurtosis0.54625365
Mean588.48592
Median Absolute Deviation (MAD)198
Skewness1.3330395
Sum83565
Variance320328.93
MonotonicityNot monotonic
2023-12-12T07:51:30.537277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1830 20
 
14.1%
330 15
 
10.6%
931 13
 
9.2%
468 8
 
5.6%
594 6
 
4.2%
193 6
 
4.2%
136 6
 
4.2%
678 6
 
4.2%
846 5
 
3.5%
268 5
 
3.5%
Other values (15) 52
36.6%
ValueCountFrequency (%)
57 4
2.8%
62 2
 
1.4%
64 3
2.1%
72 3
2.1%
88 3
2.1%
128 2
 
1.4%
136 6
4.2%
138 4
2.8%
161 5
3.5%
180 4
2.8%
ValueCountFrequency (%)
1830 20
14.1%
931 13
9.2%
846 5
 
3.5%
678 6
 
4.2%
621 4
 
2.8%
594 6
 
4.2%
468 8
 
5.6%
448 3
 
2.1%
330 15
10.6%
289 5
 
3.5%

2018년 민원발생건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.169014
Minimum0
Maximum451
Zeros85
Zeros (%)59.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:30.638099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3161
95-th percentile451
Maximum451
Range451
Interquartile range (IQR)161

Descriptive statistics

Standard deviation156.86395
Coefficient of variation (CV)1.5978967
Kurtosis0.8337851
Mean98.169014
Median Absolute Deviation (MAD)0
Skewness1.5096647
Sum13940
Variance24606.297
MonotonicityNot monotonic
2023-12-12T07:51:30.723563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 85
59.9%
451 20
 
14.1%
189 15
 
10.6%
161 5
 
3.5%
57 4
 
2.8%
88 3
 
2.1%
72 3
 
2.1%
64 3
 
2.1%
128 2
 
1.4%
62 2
 
1.4%
ValueCountFrequency (%)
0 85
59.9%
57 4
 
2.8%
62 2
 
1.4%
64 3
 
2.1%
72 3
 
2.1%
88 3
 
2.1%
128 2
 
1.4%
161 5
 
3.5%
189 15
 
10.6%
451 20
 
14.1%
ValueCountFrequency (%)
451 20
 
14.1%
189 15
 
10.6%
161 5
 
3.5%
128 2
 
1.4%
88 3
 
2.1%
72 3
 
2.1%
64 3
 
2.1%
62 2
 
1.4%
57 4
 
2.8%
0 85
59.9%

2019년 민원발생건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.62676
Minimum0
Maximum518
Zeros84
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:30.807136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3204
95-th percentile289
Maximum518
Range518
Interquartile range (IQR)204

Descriptive statistics

Standard deviation137.84685
Coefficient of variation (CV)1.3175104
Kurtosis0.16708587
Mean104.62676
Median Absolute Deviation (MAD)0
Skewness0.99902961
Sum14857
Variance19001.753
MonotonicityNot monotonic
2023-12-12T07:51:30.907842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 84
59.2%
289 25
 
17.6%
204 6
 
4.2%
190 6
 
4.2%
193 6
 
4.2%
518 4
 
2.8%
211 4
 
2.8%
180 4
 
2.8%
158 3
 
2.1%
ValueCountFrequency (%)
0 84
59.2%
158 3
 
2.1%
180 4
 
2.8%
190 6
 
4.2%
193 6
 
4.2%
204 6
 
4.2%
211 4
 
2.8%
289 25
 
17.6%
518 4
 
2.8%
ValueCountFrequency (%)
518 4
 
2.8%
289 25
 
17.6%
211 4
 
2.8%
204 6
 
4.2%
193 6
 
4.2%
190 6
 
4.2%
180 4
 
2.8%
158 3
 
2.1%
0 84
59.2%

2020년 민원발생건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.99296
Minimum0
Maximum873
Zeros70
Zeros (%)49.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:30.998824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median242
Q3357
95-th percentile873
Maximum873
Range873
Interquartile range (IQR)357

Descriptive statistics

Standard deviation307.86035
Coefficient of variation (CV)1.2314761
Kurtosis-0.27456354
Mean249.99296
Median Absolute Deviation (MAD)242
Skewness1.0076577
Sum35499
Variance94777.993
MonotonicityNot monotonic
2023-12-12T07:51:31.092543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 70
49.3%
873 20
 
14.1%
546 13
 
9.2%
242 8
 
5.6%
276 6
 
4.2%
306 6
 
4.2%
357 5
 
3.5%
268 5
 
3.5%
290 3
 
2.1%
254 3
 
2.1%
ValueCountFrequency (%)
0 70
49.3%
242 8
 
5.6%
252 3
 
2.1%
254 3
 
2.1%
268 5
 
3.5%
276 6
 
4.2%
290 3
 
2.1%
306 6
 
4.2%
357 5
 
3.5%
546 13
 
9.2%
ValueCountFrequency (%)
873 20
14.1%
546 13
9.2%
357 5
 
3.5%
306 6
 
4.2%
290 3
 
2.1%
276 6
 
4.2%
268 5
 
3.5%
254 3
 
2.1%
252 3
 
2.1%
242 8
 
5.6%

2021년 민원발생건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.69718
Minimum0
Maximum489
Zeros55
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:31.184670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median137
Q3217
95-th percentile385
Maximum489
Range489
Interquartile range (IQR)217

Descriptive statistics

Standard deviation137.42031
Coefficient of variation (CV)1.0126983
Kurtosis0.010719597
Mean135.69718
Median Absolute Deviation (MAD)137
Skewness0.83585994
Sum19269
Variance18884.34
MonotonicityNot monotonic
2023-12-12T07:51:31.286884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 55
38.7%
217 20
 
14.1%
141 15
 
10.6%
385 13
 
9.2%
226 8
 
5.6%
114 6
 
4.2%
182 6
 
4.2%
136 6
 
4.2%
489 5
 
3.5%
103 4
 
2.8%
ValueCountFrequency (%)
0 55
38.7%
103 4
 
2.8%
114 6
 
4.2%
136 6
 
4.2%
138 4
 
2.8%
141 15
 
10.6%
182 6
 
4.2%
217 20
 
14.1%
226 8
 
5.6%
385 13
 
9.2%
ValueCountFrequency (%)
489 5
 
3.5%
385 13
9.2%
226 8
 
5.6%
217 20
14.1%
182 6
 
4.2%
141 15
10.6%
138 4
 
2.8%
136 6
 
4.2%
114 6
 
4.2%
103 4
 
2.8%

현장정보
Text

UNIQUE 

Distinct142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T07:51:31.586381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2394366
Min length5

Characters and Unicode

Total characters886
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)100.0%

Sample

1st row1.png
2nd row2.png
3rd row3.png
4th row4.png
5th row5.png
ValueCountFrequency (%)
1.png 1
 
0.7%
97.png 1
 
0.7%
99.png 1
 
0.7%
92.png 1
 
0.7%
93.png 1
 
0.7%
94.png 1
 
0.7%
95.png 1
 
0.7%
96.png 1
 
0.7%
90.png 1
 
0.7%
89.png 1
 
0.7%
Other values (132) 132
93.0%
2023-12-12T07:51:31.998388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 142
16.0%
p 142
16.0%
n 142
16.0%
g 142
16.0%
1 78
8.8%
2 35
 
4.0%
3 34
 
3.8%
4 27
 
3.0%
8 24
 
2.7%
9 24
 
2.7%
Other values (4) 96
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 426
48.1%
Decimal Number 318
35.9%
Other Punctuation 142
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 78
24.5%
2 35
11.0%
3 34
10.7%
4 27
 
8.5%
8 24
 
7.5%
9 24
 
7.5%
5 24
 
7.5%
6 24
 
7.5%
7 24
 
7.5%
0 24
 
7.5%
Lowercase Letter
ValueCountFrequency (%)
p 142
33.3%
n 142
33.3%
g 142
33.3%
Other Punctuation
ValueCountFrequency (%)
. 142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 460
51.9%
Latin 426
48.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 142
30.9%
1 78
17.0%
2 35
 
7.6%
3 34
 
7.4%
4 27
 
5.9%
8 24
 
5.2%
9 24
 
5.2%
5 24
 
5.2%
6 24
 
5.2%
7 24
 
5.2%
Latin
ValueCountFrequency (%)
p 142
33.3%
n 142
33.3%
g 142
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 142
16.0%
p 142
16.0%
n 142
16.0%
g 142
16.0%
1 78
8.8%
2 35
 
4.0%
3 34
 
3.8%
4 27
 
3.0%
8 24
 
2.7%
9 24
 
2.7%
Other values (4) 96
10.8%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.908246
Minimum35.879213
Maximum35.954528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:32.119168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.879213
5-th percentile35.889914
Q135.892707
median35.898388
Q335.924382
95-th percentile35.943071
Maximum35.954528
Range0.075315
Interquartile range (IQR)0.03167475

Descriptive statistics

Standard deviation0.01962213
Coefficient of variation (CV)0.00054645192
Kurtosis-0.76183344
Mean35.908246
Median Absolute Deviation (MAD)0.007039
Skewness0.73824024
Sum5098.971
Variance0.00038502799
MonotonicityNot monotonic
2023-12-12T07:51:32.239123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.906757 2
 
1.4%
35.896745 1
 
0.7%
35.921144 1
 
0.7%
35.954155 1
 
0.7%
35.954528 1
 
0.7%
35.954237 1
 
0.7%
35.898583 1
 
0.7%
35.898194 1
 
0.7%
35.920345 1
 
0.7%
35.891956 1
 
0.7%
Other values (131) 131
92.3%
ValueCountFrequency (%)
35.879213 1
0.7%
35.879361 1
0.7%
35.879569 1
0.7%
35.879987 1
0.7%
35.88038 1
0.7%
35.889087 1
0.7%
35.889236 1
0.7%
35.889909 1
0.7%
35.890012 1
0.7%
35.890034 1
0.7%
ValueCountFrequency (%)
35.954528 1
0.7%
35.954237 1
0.7%
35.954155 1
0.7%
35.943853 1
0.7%
35.943742 1
0.7%
35.943652 1
0.7%
35.943334 1
0.7%
35.943073 1
0.7%
35.943032 1
0.7%
35.942972 1
0.7%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58705
Minimum128.54682
Maximum128.62331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T07:51:32.367320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.54682
5-th percentile128.55359
Q1128.56043
median128.5969
Q3128.6089
95-th percentile128.61903
Maximum128.62331
Range0.076489
Interquartile range (IQR)0.04846175

Descriptive statistics

Standard deviation0.025050675
Coefficient of variation (CV)0.00019481491
Kurtosis-1.687055
Mean128.58705
Median Absolute Deviation (MAD)0.0220605
Skewness-0.1323062
Sum18259.361
Variance0.00062753631
MonotonicityNot monotonic
2023-12-12T07:51:32.481267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.608976 2
 
1.4%
128.612928 1
 
0.7%
128.601739 1
 
0.7%
128.564725 1
 
0.7%
128.565024 1
 
0.7%
128.565249 1
 
0.7%
128.609953 1
 
0.7%
128.610347 1
 
0.7%
128.603584 1
 
0.7%
128.570526 1
 
0.7%
Other values (131) 131
92.3%
ValueCountFrequency (%)
128.546818 1
0.7%
128.547108 1
0.7%
128.547215 1
0.7%
128.547695 1
0.7%
128.547864 1
0.7%
128.55288 1
0.7%
128.55334 1
0.7%
128.553562 1
0.7%
128.554129 1
0.7%
128.554816 1
0.7%
ValueCountFrequency (%)
128.623307 1
0.7%
128.620546 1
0.7%
128.620438 1
0.7%
128.62013 1
0.7%
128.620087 1
0.7%
128.619959 1
0.7%
128.619483 1
0.7%
128.619034 1
0.7%
128.618886 1
0.7%
128.618677 1
0.7%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing142
Missing (%)100.0%
Memory size1.4 KiB

Interactions

2023-12-12T07:51:28.837002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:23.802498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.489775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.126024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.761383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.406352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.994747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.007720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.951436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:23.883877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.574193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.198759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.842213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.479141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.074702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.098647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:29.070285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:23.963199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.652200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.291909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.918372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.563598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.171391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.194617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:29.150615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.031680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.726695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.365434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.996015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.633826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.255202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.280831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:29.251225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.103580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.800055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.436425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.068760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.700452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.348958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.385551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:29.347284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.187571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.880261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.504797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.137381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.760939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.427557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.484548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:29.433619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.273519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.959923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.579068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.222503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.828881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.810010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.614032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:29.525664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:24.399809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.040964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:25.663236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.323956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:26.907779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:27.899087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:28.725746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:51:32.570376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주소민원발생누적건수2018년 민원발생건수2019년 민원발생건수2020년 민원발생건수2021년 민원발생건수경도위도
순번1.0000.9810.8870.8660.9130.8470.8420.8820.763
주소0.9811.0001.0001.0001.0001.0001.0000.9900.974
민원발생누적건수0.8871.0001.0000.9080.7170.9720.9540.7140.781
2018년 민원발생건수0.8661.0000.9081.0000.5980.8620.8460.8370.674
2019년 민원발생건수0.9131.0000.7170.5981.0000.6640.5740.8030.638
2020년 민원발생건수0.8471.0000.9720.8620.6641.0000.9790.6740.756
2021년 민원발생건수0.8421.0000.9540.8460.5740.9791.0000.7190.755
경도0.8820.9900.7140.8370.8030.6740.7191.0000.819
위도0.7630.9740.7810.6740.6380.7560.7550.8191.000
2023-12-12T07:51:32.668142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번민원발생누적건수2018년 민원발생건수2019년 민원발생건수2020년 민원발생건수2021년 민원발생건수경도위도주소
순번1.000-0.790-0.571-0.453-0.606-0.5800.248-0.3270.807
민원발생누적건수-0.7901.0000.1950.4710.8600.789-0.3590.1060.928
2018년 민원발생건수-0.5710.1951.0000.1720.1420.117-0.3060.6710.928
2019년 민원발생건수-0.4530.4710.1721.0000.324-0.0140.2500.1300.924
2020년 민원발생건수-0.6060.8600.1420.3241.0000.668-0.3520.1250.928
2021년 민원발생건수-0.5800.7890.117-0.0140.6681.000-0.6230.1470.928
경도0.248-0.359-0.3060.250-0.352-0.6231.000-0.3530.850
위도-0.3270.1060.6710.1300.1250.147-0.3531.0000.806
주소0.8070.9280.9280.9240.9280.9280.8500.8061.000

Missing values

2023-12-12T07:51:29.680372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:51:29.835232image/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

순번주소민원발생누적건수2018년 민원발생건수2019년 민원발생건수2020년 민원발생건수2021년 민원발생건수현장정보경도위도Unnamed: 10
01대구광역시 북구 복현동 62318304512898732171.png35.896745128.612928<NA>
12대구광역시 북구 복현동 62318304512898732172.png35.893837128.617881<NA>
23대구광역시 북구 복현동 62318304512898732173.png35.893787128.61713<NA>
34대구광역시 북구 복현동 62318304512898732174.png35.894239128.616575<NA>
45대구광역시 북구 복현동 62318304512898732175.png35.892781128.623307<NA>
56대구광역시 북구 복현동 62318304512898732176.png35.893227128.619483<NA>
67대구광역시 북구 복현동 62318304512898732177.png35.893665128.618886<NA>
78대구광역시 북구 복현동 62318304512898732178.png35.892835128.619959<NA>
89대구광역시 북구 복현동 62318304512898732179.png35.892416128.620438<NA>
910대구광역시 북구 복현동 623183045128987321710.png35.894126128.614196<NA>
순번주소민원발생누적건수2018년 민원발생건수2019년 민원발생건수2020년 민원발생건수2021년 민원발생건수현장정보경도위도Unnamed: 10
132133대구광역시 북구 서변동 1771138000138133.png35.924418128.597681<NA>
133134대구광역시 북구 서변동 1771138000138134.png35.923697128.598421<NA>
134135대구광역시 북구 서변동 1771138000138135.png35.922203128.598432<NA>
135136대구광역시 북구 서변동 1771138000138136.png35.921256128.598453<NA>
136137대구광역시 북구 산격동 1499-1136000136137.png35.895534128.606552<NA>
137138대구광역시 북구 산격동 1499-1136000136138.png35.894947128.606209<NA>
138139대구광역시 북구 산격동 1499-1136000136139.png35.894378128.607684<NA>
139140대구광역시 북구 산격동 1499-1136000136140.png35.893617128.608655<NA>
140141대구광역시 북구 산격동 1499-1136000136141.png35.893056128.607067<NA>
141142대구광역시 북구 산격동 1499-1136000136142.png35.893317128.605908<NA>