Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells21
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory57.8 B

Variable types

Text2
Numeric1
Categorical3

Dataset

Description전라남도 내 시군별 문화관광해설사 활동 인원에 대한 자료입니다. 한국어, 영어 , 일본어, 중국어, 수어 가능 문화관광해설사 활동 인원을 시군별로 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15126320/fileData.do

Alerts

비고 has constant value ""Constant
일본어 is highly overall correlated with 영어 and 1 other fieldsHigh correlation
영어 is highly overall correlated with 일본어 and 1 other fieldsHigh correlation
중국어 is highly overall correlated with 영어 and 1 other fieldsHigh correlation
비고 has 21 (95.5%) missing valuesMissing
시군 has unique valuesUnique

Reproduction

Analysis started2024-03-14 17:31:32.598091
Analysis finished2024-03-14 17:31:33.699170
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T02:31:34.522495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters35
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

Unique22 ?
Unique (%)100.0%

Sample

1st row목포시
2nd row여수시
3rd row순천시
4th row나주시
5th row광양시
ValueCountFrequency (%)
목포시 1
 
4.5%
여수시 1
 
4.5%
진도군 1
 
4.5%
완도군 1
 
4.5%
장성군 1
 
4.5%
영광군 1
 
4.5%
함평군 1
 
4.5%
무안군 1
 
4.5%
영암군 1
 
4.5%
해남군 1
 
4.5%
Other values (12) 12
54.5%
2024-03-15T02:31:35.804002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

한국어
Real number (ℝ)

Distinct15
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum5
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T02:31:36.183695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11.05
Q113.25
median17.5
Q322.75
95-th percentile27.9
Maximum28
Range23
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation6.1178895
Coefficient of variation (CV)0.33988275
Kurtosis-0.5624019
Mean18
Median Absolute Deviation (MAD)4.5
Skewness0.031568039
Sum396
Variance37.428571
MonotonicityNot monotonic
2024-03-15T02:31:36.582622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
15 3
13.6%
13 3
13.6%
28 2
 
9.1%
25 2
 
9.1%
18 2
 
9.1%
26 1
 
4.5%
23 1
 
4.5%
22 1
 
4.5%
5 1
 
4.5%
14 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
5 1
 
4.5%
11 1
 
4.5%
12 1
 
4.5%
13 3
13.6%
14 1
 
4.5%
15 3
13.6%
17 1
 
4.5%
18 2
9.1%
19 1
 
4.5%
21 1
 
4.5%
ValueCountFrequency (%)
28 2
9.1%
26 1
 
4.5%
25 2
9.1%
23 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
19 1
 
4.5%
18 2
9.1%
17 1
 
4.5%
15 3
13.6%

영어
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
13 
1
5 
2
2 
4
 
1
3
 
1

Length

Max length4
Median length4
Mean length2.7727273
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row1
2nd row4
3rd row3
4th row<NA>
5th row2

Common Values

ValueCountFrequency (%)
<NA> 13
59.1%
1 5
 
22.7%
2 2
 
9.1%
4 1
 
4.5%
3 1
 
4.5%

Length

2024-03-15T02:31:36.953401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:31:37.309613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 13
59.1%
1 5
 
22.7%
2 2
 
9.1%
4 1
 
4.5%
3 1
 
4.5%

일본어
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
11 
1
7 
2
2 
9
 
1
4
 
1

Length

Max length4
Median length2.5
Mean length2.5
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row1
2nd row9
3rd row4
4th row<NA>
5th row2

Common Values

ValueCountFrequency (%)
<NA> 11
50.0%
1 7
31.8%
2 2
 
9.1%
9 1
 
4.5%
4 1
 
4.5%

Length

2024-03-15T02:31:37.719931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:31:38.071156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 11
50.0%
1 7
31.8%
2 2
 
9.1%
9 1
 
4.5%
4 1
 
4.5%

중국어
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
11 
1
6 
2
3 
5
 
1
3
 
1

Length

Max length4
Median length2.5
Mean length2.5
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row1
2nd row5
3rd row3
4th row<NA>
5th row1

Common Values

ValueCountFrequency (%)
<NA> 11
50.0%
1 6
27.3%
2 3
 
13.6%
5 1
 
4.5%
3 1
 
4.5%

Length

2024-03-15T02:31:38.418209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:31:38.658643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 11
50.0%
1 6
27.3%
2 3
 
13.6%
5 1
 
4.5%
3 1
 
4.5%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing21
Missing (%)95.5%
Memory size304.0 B
2024-03-15T02:31:39.161318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row수어 가능 1명
ValueCountFrequency (%)
수어 1
33.3%
가능 1
33.3%
1명 1
33.3%
2024-03-15T02:31:39.939446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
25.0%
수 1
12.5%
어 1
12.5%
가 1
12.5%
능 1
12.5%
1 1
12.5%
명 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
62.5%
Space Separator 2
 
25.0%
Decimal Number 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 1
20.0%
어 1
20.0%
가 1
20.0%
능 1
20.0%
명 1
20.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
62.5%
Common 3
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 1
20.0%
어 1
20.0%
가 1
20.0%
능 1
20.0%
명 1
20.0%
Common
ValueCountFrequency (%)
2
66.7%
1 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
62.5%
ASCII 3
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
66.7%
1 1
33.3%
Hangul
ValueCountFrequency (%)
수 1
20.0%
어 1
20.0%
가 1
20.0%
능 1
20.0%
명 1
20.0%

Interactions

2024-03-15T02:31:32.884428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:31:40.083622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군한국어영어일본어중국어
시군1.0001.0001.0001.0001.000
한국어1.0001.0000.6200.7170.941
영어1.0000.6201.0001.0000.956
일본어1.0000.7171.0001.0000.966
중국어1.0000.9410.9560.9661.000
2024-03-15T02:31:40.249567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중국어일본어영어
중국어1.0000.7300.667
일본어0.7301.0001.000
영어0.6671.0001.000
2024-03-15T02:31:40.400308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
한국어영어일본어중국어
한국어1.0000.0000.3820.388
영어0.0001.0001.0000.667
일본어0.3821.0001.0000.730
중국어0.3880.6670.7301.000

Missing values

2024-03-15T02:31:33.197160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:31:33.565675image/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

시군한국어영어일본어중국어비고
0목포시26111<NA>
1여수시28495수어 가능 1명
2순천시25343<NA>
3나주시23<NA><NA><NA><NA>
4광양시22221<NA>
5담양군15<NA><NA><NA><NA>
6곡성군52<NA>1<NA>
7구례군14<NA>12<NA>
8고흥군15<NA>11<NA>
9보성군12<NA><NA><NA><NA>
시군한국어영어일본어중국어비고
12강진군28<NA><NA><NA><NA>
13해남군19<NA>2<NA><NA>
14영암군21111<NA>
15무안군13<NA>12<NA>
16함평군17<NA><NA><NA><NA>
17영광군18<NA><NA><NA><NA>
18장성군13<NA><NA><NA><NA>
19완도군25<NA><NA><NA><NA>
20진도군1811<NA><NA>
21신안군11<NA><NA>1<NA>