Overview

Dataset statistics

Number of variables8
Number of observations620
Missing cells357
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.5 KiB
Average record size in memory70.2 B

Variable types

Numeric6
Categorical2

Dataset

Description경기도 불법광고물 단속 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=IMORMEV8I72COW1QUIX118785722&infSeq=1

Alerts

현수막수(개) is highly overall correlated with 벽보수(개)High correlation
벽보수(개) is highly overall correlated with 현수막수(개) and 1 other fieldsHigh correlation
전단수(개) is highly overall correlated with 벽보수(개)High correlation
입간판수(개) has 44 (7.1%) missing valuesMissing
벽보수(개) has 10 (1.6%) missing valuesMissing
전단수(개) has 41 (6.6%) missing valuesMissing
기타수(개) has 261 (42.1%) missing valuesMissing
입간판수(개) has 30 (4.8%) zerosZeros
전단수(개) has 11 (1.8%) zerosZeros
기타수(개) has 55 (8.9%) zerosZeros

Reproduction

Analysis started2024-04-29 13:20:37.262945
Analysis finished2024-04-29 13:20:42.826853
Duration5.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-29T22:20:42.875548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2023
Maximum2023
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.961174
Coefficient of variation (CV)0.0009711667
Kurtosis-1.001653
Mean2019.4
Median Absolute Deviation (MAD)1.5
Skewness0.46998547
Sum1252028
Variance3.8462036
MonotonicityDecreasing
2024-04-29T22:20:42.964262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 124
20.0%
2018 124
20.0%
2017 124
20.0%
2023 62
10.0%
2022 62
10.0%
2021 62
10.0%
2020 62
10.0%
ValueCountFrequency (%)
2017 124
20.0%
2018 124
20.0%
2019 124
20.0%
2020 62
10.0%
2021 62
10.0%
2022 62
10.0%
2023 62
10.0%
ValueCountFrequency (%)
2023 62
10.0%
2022 62
10.0%
2021 62
10.0%
2020 62
10.0%
2019 124
20.0%
2018 124
20.0%
2017 124
20.0%

집계분기
Categorical

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2분기
124 
1분기
124 
하반기
93 
상반기
93 
4분기
93 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2분기 124
20.0%
1분기 124
20.0%
하반기 93
15.0%
상반기 93
15.0%
4분기 93
15.0%
3분기 93
15.0%

Length

2024-04-29T22:20:43.087035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T22:20:43.196292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2분기 124
20.0%
1분기 124
20.0%
하반기 93
15.0%
상반기 93
15.0%
4분기 93
15.0%
3분기 93
15.0%

시군명
Categorical

Distinct31
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
가평군
 
20
고양시
 
20
과천시
 
20
광명시
 
20
광주시
 
20
Other values (26)
520 

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시

Common Values

ValueCountFrequency (%)
가평군 20
 
3.2%
고양시 20
 
3.2%
과천시 20
 
3.2%
광명시 20
 
3.2%
광주시 20
 
3.2%
구리시 20
 
3.2%
군포시 20
 
3.2%
김포시 20
 
3.2%
남양주시 20
 
3.2%
동두천시 20
 
3.2%
Other values (21) 420
67.7%

Length

2024-04-29T22:20:43.310667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가평군 20
 
3.2%
안양시 20
 
3.2%
하남시 20
 
3.2%
포천시 20
 
3.2%
평택시 20
 
3.2%
파주시 20
 
3.2%
이천시 20
 
3.2%
의정부시 20
 
3.2%
의왕시 20
 
3.2%
용인시 20
 
3.2%
Other values (21) 420
67.7%

현수막수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct616
Distinct (%)99.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean39257.11
Minimum300
Maximum434243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-29T22:20:43.421001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1399.3
Q16779
median22797
Q350374.5
95-th percentile130644
Maximum434243
Range433943
Interquartile range (IQR)43595.5

Descriptive statistics

Standard deviation51754.217
Coefficient of variation (CV)1.31834
Kurtosis14.355193
Mean39257.11
Median Absolute Deviation (MAD)17947
Skewness3.1830075
Sum24300151
Variance2.678499 × 109
MonotonicityNot monotonic
2024-04-29T22:20:43.546688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6952 2
 
0.3%
4731 2
 
0.3%
8900 2
 
0.3%
5855 1
 
0.2%
74341 1
 
0.2%
3877 1
 
0.2%
79676 1
 
0.2%
45676 1
 
0.2%
73642 1
 
0.2%
34147 1
 
0.2%
Other values (606) 606
97.7%
ValueCountFrequency (%)
300 1
0.2%
401 1
0.2%
510 1
0.2%
592 1
0.2%
600 1
0.2%
648 1
0.2%
650 1
0.2%
653 1
0.2%
723 1
0.2%
727 1
0.2%
ValueCountFrequency (%)
434243 1
0.2%
389283 1
0.2%
331085 1
0.2%
297170 1
0.2%
285285 1
0.2%
284641 1
0.2%
284324 1
0.2%
269646 1
0.2%
264851 1
0.2%
224670 1
0.2%

입간판수(개)
Real number (ℝ)

MISSING  ZEROS 

Distinct368
Distinct (%)63.9%
Missing44
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean7303.9531
Minimum0
Maximum1414350
Zeros30
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-29T22:20:43.674173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.75
median161.5
Q3504
95-th percentile5137.5
Maximum1414350
Range1414350
Interquartile range (IQR)481.25

Descriptive statistics

Standard deviation68382.534
Coefficient of variation (CV)9.3624005
Kurtosis320.00736
Mean7303.9531
Median Absolute Deviation (MAD)155.5
Skewness16.574375
Sum4207077
Variance4.676171 × 109
MonotonicityNot monotonic
2024-04-29T22:20:43.808044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
4.8%
1 12
 
1.9%
2 11
 
1.8%
3 9
 
1.5%
5 8
 
1.3%
6 8
 
1.3%
10 8
 
1.3%
14 7
 
1.1%
50 6
 
1.0%
46 6
 
1.0%
Other values (358) 471
76.0%
(Missing) 44
 
7.1%
ValueCountFrequency (%)
0 30
4.8%
1 12
 
1.9%
2 11
 
1.8%
3 9
 
1.5%
4 3
 
0.5%
5 8
 
1.3%
6 8
 
1.3%
7 5
 
0.8%
8 1
 
0.2%
9 5
 
0.8%
ValueCountFrequency (%)
1414350 1
0.2%
428596 1
0.2%
427010 1
0.2%
424589 1
0.2%
281757 1
0.2%
169793 1
0.2%
131792 1
0.2%
129471 1
0.2%
88294 1
0.2%
74554 1
0.2%

벽보수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct595
Distinct (%)97.5%
Missing10
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean172920.52
Minimum0
Maximum4352241
Zeros6
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-29T22:20:43.947034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.05
Q13723.25
median19353.5
Q389371.25
95-th percentile925988.9
Maximum4352241
Range4352241
Interquartile range (IQR)85648

Descriptive statistics

Standard deviation451614.08
Coefficient of variation (CV)2.6116858
Kurtosis31.918797
Mean172920.52
Median Absolute Deviation (MAD)18707
Skewness5.0101897
Sum1.0548152 × 108
Variance2.0395527 × 1011
MonotonicityNot monotonic
2024-04-29T22:20:44.072319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
1.0%
2 3
 
0.5%
4675 2
 
0.3%
47 2
 
0.3%
38 2
 
0.3%
291 2
 
0.3%
72 2
 
0.3%
40 2
 
0.3%
267 2
 
0.3%
10 2
 
0.3%
Other values (585) 585
94.4%
(Missing) 10
 
1.6%
ValueCountFrequency (%)
0 6
1.0%
2 3
0.5%
10 2
 
0.3%
12 1
 
0.2%
22 1
 
0.2%
23 1
 
0.2%
33 1
 
0.2%
35 1
 
0.2%
38 2
 
0.3%
39 1
 
0.2%
ValueCountFrequency (%)
4352241 1
0.2%
3838403 1
0.2%
3510859 1
0.2%
3450563 1
0.2%
2523476 1
0.2%
2386839 1
0.2%
2316388 1
0.2%
2256223 1
0.2%
2101239 1
0.2%
1720338 1
0.2%

전단수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct556
Distinct (%)96.0%
Missing41
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean559084.75
Minimum0
Maximum16349348
Zeros11
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-29T22:20:44.206189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q14303
median34164
Q3355840.5
95-th percentile3111543.7
Maximum16349348
Range16349348
Interquartile range (IQR)351537.5

Descriptive statistics

Standard deviation1369775.3
Coefficient of variation (CV)2.4500315
Kurtosis38.680159
Mean559084.75
Median Absolute Deviation (MAD)33984
Skewness4.9839818
Sum3.2371007 × 108
Variance1.8762843 × 1012
MonotonicityNot monotonic
2024-04-29T22:20:44.343549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.8%
20 4
 
0.6%
9 3
 
0.5%
40 3
 
0.5%
180 2
 
0.3%
10 2
 
0.3%
30 2
 
0.3%
194329 2
 
0.3%
29 2
 
0.3%
70 2
 
0.3%
Other values (546) 546
88.1%
(Missing) 41
 
6.6%
ValueCountFrequency (%)
0 11
1.8%
3 1
 
0.2%
5 1
 
0.2%
9 3
 
0.5%
10 2
 
0.3%
11 1
 
0.2%
12 1
 
0.2%
15 1
 
0.2%
20 4
 
0.6%
21 1
 
0.2%
ValueCountFrequency (%)
16349348 1
0.2%
10502143 1
0.2%
7808926 1
0.2%
7289595 1
0.2%
5930171 1
0.2%
5737599 1
0.2%
5253057 1
0.2%
5204771 1
0.2%
5174784 1
0.2%
5027987 1
0.2%

기타수(개)
Real number (ℝ)

MISSING  ZEROS 

Distinct257
Distinct (%)71.6%
Missing261
Missing (%)42.1%
Infinite0
Infinite (%)0.0%
Mean187348.64
Minimum0
Maximum6849571
Zeros55
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-29T22:20:44.479599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median351
Q39493
95-th percentile871551.8
Maximum6849571
Range6849571
Interquartile range (IQR)9480

Descriptive statistics

Standard deviation668762.83
Coefficient of variation (CV)3.5696168
Kurtosis37.450486
Mean187348.64
Median Absolute Deviation (MAD)351
Skewness5.4909857
Sum67258160
Variance4.4724373 × 1011
MonotonicityNot monotonic
2024-04-29T22:20:44.611466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
8.9%
1 7
 
1.1%
10 4
 
0.6%
7 4
 
0.6%
32 4
 
0.6%
42 4
 
0.6%
236 4
 
0.6%
25 3
 
0.5%
12 3
 
0.5%
4 3
 
0.5%
Other values (247) 268
43.2%
(Missing) 261
42.1%
ValueCountFrequency (%)
0 55
8.9%
1 7
 
1.1%
2 3
 
0.5%
3 3
 
0.5%
4 3
 
0.5%
5 2
 
0.3%
7 4
 
0.6%
8 2
 
0.3%
10 4
 
0.6%
11 3
 
0.5%
ValueCountFrequency (%)
6849571 1
0.2%
4103729 1
0.2%
3629282 1
0.2%
3433027 1
0.2%
3299269 1
0.2%
3250214 1
0.2%
2857732 1
0.2%
2810390 1
0.2%
2350428 1
0.2%
2262718 1
0.2%

Interactions

2024-04-29T22:20:41.787736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:38.802118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.557098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.088169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.676231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.227454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.871323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:38.968516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.644047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.181096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.776321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.316187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.951783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.187876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.719697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.279911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.866009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.399402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:42.050147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.283426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.814527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.383592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.965856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.512787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:42.125971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.371641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.896887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.483474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.054850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.598590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:42.217659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.465300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:39.997513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:40.580305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.148671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:20:41.695244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T22:20:44.713547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도집계분기시군명현수막수(개)입간판수(개)벽보수(개)전단수(개)기타수(개)
집계년도1.0000.8530.0000.000NaN0.1110.1210.127
집계분기0.8531.0000.0000.1700.1190.1620.1170.000
시군명0.0000.0001.0000.6240.0000.5090.5760.568
현수막수(개)0.0000.1700.6241.0000.0000.4090.4460.455
입간판수(개)NaN0.1190.0000.0001.0000.8060.0000.000
벽보수(개)0.1110.1620.5090.4090.8061.0000.6660.388
전단수(개)0.1210.1170.5760.4460.0000.6661.0000.000
기타수(개)0.1270.0000.5680.4550.0000.3880.0001.000
2024-04-29T22:20:44.818412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명집계분기
시군명1.0000.000
집계분기0.0001.000
2024-04-29T22:20:45.083907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도현수막수(개)입간판수(개)벽보수(개)전단수(개)기타수(개)집계분기시군명
집계년도1.000-0.011-0.1880.0160.145-0.0780.4660.000
현수막수(개)-0.0111.0000.4920.5530.4060.2540.0900.268
입간판수(개)-0.1880.4921.0000.4820.3100.3310.0770.000
벽보수(개)0.0160.5530.4821.0000.6540.2220.0810.212
전단수(개)0.1450.4060.3100.6541.0000.3220.0700.276
기타수(개)-0.0780.2540.3310.2220.3221.0000.0000.267
집계분기0.4660.0900.0770.0810.0700.0001.0000.000
시군명0.0000.2680.0000.2120.2760.2670.0001.000

Missing values

2024-04-29T22:20:42.512305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T22:20:42.646320image/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.
2024-04-29T22:20:42.762485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

집계년도집계분기시군명현수막수(개)입간판수(개)벽보수(개)전단수(개)기타수(개)
02023하반기가평군221607500
12023하반기고양시651344045124729825140
22023하반기과천시34130548031250
32023하반기광명시805811126010487855725
42023하반기광주시9301244180443200
52023하반기구리시100311937201042739352605
62023하반기군포시8676311258188022027
72023하반기김포시240295057796263394511925
82023하반기남양주시741013301631626862774
92023하반기동두천시315521181502437108
집계년도집계분기시군명현수막수(개)입간판수(개)벽보수(개)전단수(개)기타수(개)
61020171분기오산시57871469202905090<NA>
61120171분기용인시851581317922112035131190
61220171분기의왕시501137636426012124333
61320171분기의정부시592034285961383030289170
61420171분기이천시3426119324847512<NA>
61520171분기파주시40225461742546027086
61620171분기평택시512926489487653671989
61720171분기포천시8519450159023<NA><NA>
61820171분기하남시178006762671948239<NA>
61920171분기화성시130224<NA>202554211049488