Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory57.9 B

Variable types

Text1
Numeric5

Dataset

Description자치구,2013년,2014년,2015년,2016년,2017년
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15068/S/1/datasetView.do

Alerts

2013년 is highly overall correlated with 2014년 and 3 other fieldsHigh correlation
2014년 is highly overall correlated with 2013년 and 3 other fieldsHigh correlation
2015년 is highly overall correlated with 2013년 and 3 other fieldsHigh correlation
2016년 is highly overall correlated with 2013년 and 3 other fieldsHigh correlation
2017년 is highly overall correlated with 2013년 and 3 other fieldsHigh correlation
자치구 has unique valuesUnique
2016년 has unique valuesUnique
2017년 has unique valuesUnique

Reproduction

Analysis started2024-03-13 19:11:25.294386
Analysis finished2024-03-13 19:11:27.139900
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-14T04:11:27.207102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6296296
Min length1

Characters and Unicode

Total characters125
Distinct characters40
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row
2nd row중앙차로
3rd row종 로 구
4th row중 구
5th row용 산 구
ValueCountFrequency (%)
23
32.9%
4
 
5.7%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
Other values (27) 27
38.6%
2024-03-14T04:11:27.452580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43
34.4%
26
20.8%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
2
 
1.6%
2
 
1.6%
2
 
1.6%
Other values (30) 33
26.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
65.6%
Space Separator 43
34.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
31.7%
4
 
4.9%
4
 
4.9%
3
 
3.7%
3
 
3.7%
3
 
3.7%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (29) 31
37.8%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
65.6%
Common 43
34.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
31.7%
4
 
4.9%
4
 
4.9%
3
 
3.7%
3
 
3.7%
3
 
3.7%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (29) 31
37.8%
Common
ValueCountFrequency (%)
43
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
65.6%
ASCII 43
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43
100.0%
Hangul
ValueCountFrequency (%)
26
31.7%
4
 
4.9%
4
 
4.9%
3
 
3.7%
3
 
3.7%
3
 
3.7%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (29) 31
37.8%

2013년
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.55556
Minimum6
Maximum4476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T04:11:27.549998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile58.3
Q190.5
median130
Q3186.5
95-th percentile806.8
Maximum4476
Range4470
Interquartile range (IQR)96

Descriptive statistics

Standard deviation847.12447
Coefficient of variation (CV)2.5550001
Kurtosis24.402735
Mean331.55556
Median Absolute Deviation (MAD)51
Skewness4.8629888
Sum8952
Variance717619.87
MonotonicityNot monotonic
2024-03-14T04:11:27.630846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
130 2
 
7.4%
129 2
 
7.4%
4476 1
 
3.7%
182 1
 
3.7%
136 1
 
3.7%
132 1
 
3.7%
946 1
 
3.7%
6 1
 
3.7%
62 1
 
3.7%
201 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
6 1
3.7%
58 1
3.7%
59 1
3.7%
62 1
3.7%
66 1
3.7%
79 1
3.7%
80 1
3.7%
101 1
3.7%
111 1
3.7%
112 1
3.7%
ValueCountFrequency (%)
4476 1
3.7%
946 1
3.7%
482 1
3.7%
250 1
3.7%
212 1
3.7%
201 1
3.7%
191 1
3.7%
182 1
3.7%
176 1
3.7%
169 1
3.7%

2014년
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.77778
Minimum6
Maximum4884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T04:11:27.729632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile38.3
Q193.5
median140
Q3236
95-th percentile789.4
Maximum4884
Range4878
Interquartile range (IQR)142.5

Descriptive statistics

Standard deviation920.99078
Coefficient of variation (CV)2.5457362
Kurtosis24.803912
Mean361.77778
Median Absolute Deviation (MAD)77
Skewness4.910379
Sum9768
Variance848224.03
MonotonicityNot monotonic
2024-03-14T04:11:27.826738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
129 2
 
7.4%
4884 1
 
3.7%
178 1
 
3.7%
238 1
 
3.7%
300 1
 
3.7%
946 1
 
3.7%
6 1
 
3.7%
35 1
 
3.7%
211 1
 
3.7%
72 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
6 1
3.7%
35 1
3.7%
46 1
3.7%
59 1
3.7%
63 1
3.7%
72 1
3.7%
79 1
3.7%
108 1
3.7%
121 1
3.7%
129 2
7.4%
ValueCountFrequency (%)
4884 1
3.7%
946 1
3.7%
424 1
3.7%
300 1
3.7%
248 1
3.7%
241 1
3.7%
238 1
3.7%
234 1
3.7%
219 1
3.7%
211 1
3.7%

2015년
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.59259
Minimum2
Maximum5138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T04:11:27.915153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile52.7
Q1103.5
median168
Q3247
95-th percentile761.2
Maximum5138
Range5136
Interquartile range (IQR)143.5

Descriptive statistics

Standard deviation965.95641
Coefficient of variation (CV)2.5380326
Kurtosis25.144118
Mean380.59259
Median Absolute Deviation (MAD)70
Skewness4.9533318
Sum10276
Variance933071.79
MonotonicityNot monotonic
2024-03-14T04:11:28.008201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
256 2
 
7.4%
84 2
 
7.4%
5138 1
 
3.7%
230 1
 
3.7%
372 1
 
3.7%
238 1
 
3.7%
928 1
 
3.7%
2 1
 
3.7%
142 1
 
3.7%
109 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
2 1
3.7%
50 1
3.7%
59 1
3.7%
79 1
3.7%
84 2
7.4%
98 1
3.7%
109 1
3.7%
122 1
3.7%
133 1
3.7%
142 1
3.7%
ValueCountFrequency (%)
5138 1
3.7%
928 1
3.7%
372 1
3.7%
346 1
3.7%
273 1
3.7%
256 2
7.4%
238 1
3.7%
230 1
3.7%
229 1
3.7%
200 1
3.7%

2016년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean417.77778
Minimum10
Maximum5640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T04:11:28.090564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile51
Q1126.5
median199
Q3266.5
95-th percentile773.4
Maximum5640
Range5630
Interquartile range (IQR)140

Descriptive statistics

Standard deviation1057.6953
Coefficient of variation (CV)2.5317175
Kurtosis25.42731
Mean417.77778
Median Absolute Deviation (MAD)69
Skewness4.9888011
Sum11280
Variance1118719.3
MonotonicityNot monotonic
2024-03-14T04:11:28.172877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5640 1
 
3.7%
371 1
 
3.7%
380 1
 
3.7%
265 1
 
3.7%
942 1
 
3.7%
10 1
 
3.7%
101 1
 
3.7%
143 1
 
3.7%
117 1
 
3.7%
99 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
10 1
3.7%
42 1
3.7%
72 1
3.7%
79 1
3.7%
99 1
3.7%
101 1
3.7%
117 1
3.7%
136 1
3.7%
143 1
3.7%
152 1
3.7%
ValueCountFrequency (%)
5640 1
3.7%
942 1
3.7%
380 1
3.7%
371 1
3.7%
305 1
3.7%
272 1
3.7%
268 1
3.7%
265 1
3.7%
258 1
3.7%
248 1
3.7%

2017년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439.92593
Minimum36
Maximum5939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T04:11:28.263258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile51.4
Q1135
median213
Q3283
95-th percentile783.4
Maximum5939
Range5903
Interquartile range (IQR)148

Descriptive statistics

Standard deviation1112.3497
Coefficient of variation (CV)2.5284932
Kurtosis25.573359
Mean439.92593
Median Absolute Deviation (MAD)74
Skewness5.0073054
Sum11878
Variance1237321.9
MonotonicityNot monotonic
2024-03-14T04:11:28.351587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5939 1
 
3.7%
369 1
 
3.7%
404 1
 
3.7%
281 1
 
3.7%
946 1
 
3.7%
57 1
 
3.7%
131 1
 
3.7%
160 1
 
3.7%
147 1
 
3.7%
123 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
36 1
3.7%
49 1
3.7%
57 1
3.7%
106 1
3.7%
110 1
3.7%
123 1
3.7%
131 1
3.7%
139 1
3.7%
147 1
3.7%
158 1
3.7%
ValueCountFrequency (%)
5939 1
3.7%
946 1
3.7%
404 1
3.7%
369 1
3.7%
333 1
3.7%
308 1
3.7%
285 1
3.7%
281 1
3.7%
254 1
3.7%
251 1
3.7%

Interactions

2024-03-14T04:11:26.707257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.464017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.765334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.055137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.397250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.763012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.521227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.819848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.109812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.453322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.822889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.591833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.879043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.172228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.522727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.888220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.650441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.938114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.245734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.582789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.951543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.705535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:25.994281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.319858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:11:26.641140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T04:11:28.437114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구2013년2014년2015년2016년2017년
자치구1.0001.0001.0001.0001.0001.000
2013년1.0001.0001.0001.0001.0001.000
2014년1.0001.0001.0001.0001.0001.000
2015년1.0001.0001.0001.0001.0001.000
2016년1.0001.0001.0001.0001.0001.000
2017년1.0001.0001.0001.0001.0001.000
2024-03-14T04:11:28.538310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년2014년2015년2016년2017년
2013년1.0000.8840.8600.8390.834
2014년0.8841.0000.9170.8900.891
2015년0.8600.9171.0000.9940.985
2016년0.8390.8900.9941.0000.984
2017년0.8340.8910.9850.9841.000

Missing values

2024-03-14T04:11:27.034752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T04:11:27.110453image/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

자치구2013년2014년2015년2016년2017년
044764884513856405939
1중앙차로482424346371369
2종 로 구130129178210235
3중 구130234256272308
4용 산 구147132168198228
5성 동 구58635972106
6광 진 구112121133165139
7동대문구250241256268285
8중 랑 구7979797949
9성 북 구1114698136110
자치구2013년2014년2015년2016년2017년
17강 서 구80140153183213
18구 로 구212219273305333
19금 천 구59728499123
20영등포구201211109117147
21동 작 구129129142143160
22관 악 구623584101131
23서 초 구6621057
24강 남 구946946928942946
25송 파 구132300238265281
26강 동 구136238372380404