Overview

Dataset statistics

Number of variables4
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1016.0 B
Average record size in memory39.1 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description자치구코드,자치구명,법정동명,업소수
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-11355/S/1/datasetView.do

Alerts

자치구코드 has constant value ""Constant
자치구명 has constant value ""Constant
법정동명 has unique valuesUnique

Reproduction

Analysis started2024-05-03 20:15:39.810548
Analysis finished2024-05-03 20:15:40.661666
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
3130000
26 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 26
100.0%

Length

2024-05-03T20:15:40.868652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:15:41.151103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 26
100.0%

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
마포구
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 26
100.0%

Length

2024-05-03T20:15:41.454342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:15:41.737768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 26
100.0%

법정동명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-05-03T20:15:42.156378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0384615
Min length2

Characters and Unicode

Total characters79
Distinct characters39
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

Unique26 ?
Unique (%)100.0%

Sample

1st row공덕동
2nd row구수동
3rd row노고산동
4th row당인동
5th row대현동
ValueCountFrequency (%)
공덕동 1
 
3.8%
구수동 1
 
3.8%
합정동 1
 
3.8%
하중동 1
 
3.8%
창전동 1
 
3.8%
중동 1
 
3.8%
용강동 1
 
3.8%
염리동 1
 
3.8%
연남동 1
 
3.8%
아현동 1
 
3.8%
Other values (16) 16
61.5%
2024-05-03T20:15:42.986481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
34.2%
현 3
 
3.8%
수 3
 
3.8%
신 3
 
3.8%
중 2
 
2.5%
상 2
 
2.5%
덕 2
 
2.5%
교 2
 
2.5%
대 2
 
2.5%
공 2
 
2.5%
Other values (29) 31
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
34.2%
현 3
 
3.8%
수 3
 
3.8%
신 3
 
3.8%
중 2
 
2.5%
상 2
 
2.5%
덕 2
 
2.5%
교 2
 
2.5%
대 2
 
2.5%
공 2
 
2.5%
Other values (29) 31
39.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
34.2%
현 3
 
3.8%
수 3
 
3.8%
신 3
 
3.8%
중 2
 
2.5%
상 2
 
2.5%
덕 2
 
2.5%
교 2
 
2.5%
대 2
 
2.5%
공 2
 
2.5%
Other values (29) 31
39.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
34.2%
현 3
 
3.8%
수 3
 
3.8%
신 3
 
3.8%
중 2
 
2.5%
상 2
 
2.5%
덕 2
 
2.5%
교 2
 
2.5%
대 2
 
2.5%
공 2
 
2.5%
Other values (29) 31
39.2%

업소수
Real number (ℝ)

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.34615
Minimum1
Maximum493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-05-03T20:15:43.372565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122.25
median81.5
Q3158
95-th percentile233.5
Maximum493
Range492
Interquartile range (IQR)135.75

Descriptive statistics

Standard deviation107.74078
Coefficient of variation (CV)1.0527096
Kurtosis5.8987676
Mean102.34615
Median Absolute Deviation (MAD)67
Skewness2.0071776
Sum2661
Variance11608.075
MonotonicityNot monotonic
2024-05-03T20:15:43.948212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 3
 
11.5%
143 1
 
3.8%
47 1
 
3.8%
6 1
 
3.8%
163 1
 
3.8%
52 1
 
3.8%
26 1
 
3.8%
37 1
 
3.8%
69 1
 
3.8%
174 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
1 3
11.5%
4 1
 
3.8%
6 1
 
3.8%
9 1
 
3.8%
21 1
 
3.8%
26 1
 
3.8%
37 1
 
3.8%
47 1
 
3.8%
52 1
 
3.8%
69 1
 
3.8%
ValueCountFrequency (%)
493 1
3.8%
237 1
3.8%
223 1
3.8%
178 1
3.8%
177 1
3.8%
174 1
3.8%
163 1
3.8%
143 1
3.8%
117 1
3.8%
112 1
3.8%

Interactions

2024-05-03T20:15:39.970816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T20:15:44.187453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업소수
법정동명1.0001.000
업소수1.0001.000

Missing values

2024-05-03T20:15:40.287193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T20:15:40.559370image/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

자치구코드자치구명법정동명업소수
03130000마포구공덕동143
13130000마포구구수동9
23130000마포구노고산동108
33130000마포구당인동1
43130000마포구대현동1
53130000마포구대흥동84
63130000마포구도화동177
73130000마포구동교동237
83130000마포구마포동21
93130000마포구망원동223
자치구코드자치구명법정동명업소수
163130000마포구신정동4
173130000마포구아현동99
183130000마포구연남동174
193130000마포구염리동69
203130000마포구용강동37
213130000마포구중동26
223130000마포구창전동52
233130000마포구하중동1
243130000마포구합정동163
253130000마포구현석동6