Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory60.7 B

Variable types

Text3
Categorical2
Numeric1
Boolean1

Dataset

Description국유림경영시스템 관련 기관정보(기관명,기관코드, 상위기관코드 등)의 정보
Author산림청
URLhttps://www.data.go.kr/data/15071089/fileData.do

Alerts

사용여부 has constant value ""Constant
상위기관ID is highly overall correlated with 정렬순서 and 1 other fieldsHigh correlation
기관구분코드 is highly overall correlated with 정렬순서 and 1 other fieldsHigh correlation
정렬순서 is highly overall correlated with 기관구분코드 and 1 other fieldsHigh correlation
기관명 has unique valuesUnique
기관약어명 has unique valuesUnique
정렬순서 has unique valuesUnique
조직ID has unique valuesUnique
정렬순서 has 1 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-12 08:49:39.179759
Analysis finished2023-12-12 08:49:39.850847
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T17:49:40.039608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.4722222
Min length2

Characters and Unicode

Total characters269
Distinct characters55
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

Unique36 ?
Unique (%)100.0%

Sample

1st row중부지방산림청
2nd row충주국유림관리소
3rd row보은국유림관리소
4th row단양국유림관리소
5th row부여국유림관리소
ValueCountFrequency (%)
중부지방산림청 1
 
2.8%
충주국유림관리소 1
 
2.8%
영덕국유림관리소 1
 
2.8%
수원국유림관리소 1
 
2.8%
인제국유림관리소 1
 
2.8%
양구국유림관리소 1
 
2.8%
민북지역국유림관리소 1
 
2.8%
남부지방산림청 1
 
2.8%
영주국유림관리소 1
 
2.8%
구미국유림관리소 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T17:49:40.427602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
림 34
12.6%
리 28
 
10.4%
소 28
 
10.4%
국 28
 
10.4%
유 28
 
10.4%
관 28
 
10.4%
산 7
 
2.6%
지 6
 
2.2%
청 6
 
2.2%
부 6
 
2.2%
Other values (45) 70
26.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 269
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
림 34
12.6%
리 28
 
10.4%
소 28
 
10.4%
국 28
 
10.4%
유 28
 
10.4%
관 28
 
10.4%
산 7
 
2.6%
지 6
 
2.2%
청 6
 
2.2%
부 6
 
2.2%
Other values (45) 70
26.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 269
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
림 34
12.6%
리 28
 
10.4%
소 28
 
10.4%
국 28
 
10.4%
유 28
 
10.4%
관 28
 
10.4%
산 7
 
2.6%
지 6
 
2.2%
청 6
 
2.2%
부 6
 
2.2%
Other values (45) 70
26.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 269
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
림 34
12.6%
리 28
 
10.4%
소 28
 
10.4%
국 28
 
10.4%
유 28
 
10.4%
관 28
 
10.4%
산 7
 
2.6%
지 6
 
2.2%
청 6
 
2.2%
부 6
 
2.2%
Other values (45) 70
26.0%

기관약어명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T17:49:40.647491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3611111
Min length2

Characters and Unicode

Total characters85
Distinct characters47
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

Unique36 ?
Unique (%)100.0%

Sample

1st row중부지청
2nd row충주
3rd row보은
4th row단양
5th row부여
ValueCountFrequency (%)
중부지청 1
 
2.8%
충주 1
 
2.8%
영덕 1
 
2.8%
수원 1
 
2.8%
인제 1
 
2.8%
양구 1
 
2.8%
민북 1
 
2.8%
남부지청 1
 
2.8%
영주 1
 
2.8%
구미 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T17:49:41.029618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 7
 
8.2%
부 6
 
7.1%
지 6
 
7.1%
양 6
 
7.1%
주 5
 
5.9%
영 4
 
4.7%
제 3
 
3.5%
천 3
 
3.5%
서 2
 
2.4%
울 2
 
2.4%
Other values (37) 41
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 7
 
8.2%
부 6
 
7.1%
지 6
 
7.1%
양 6
 
7.1%
주 5
 
5.9%
영 4
 
4.7%
제 3
 
3.5%
천 3
 
3.5%
서 2
 
2.4%
울 2
 
2.4%
Other values (37) 41
48.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 7
 
8.2%
부 6
 
7.1%
지 6
 
7.1%
양 6
 
7.1%
주 5
 
5.9%
영 4
 
4.7%
제 3
 
3.5%
천 3
 
3.5%
서 2
 
2.4%
울 2
 
2.4%
Other values (37) 41
48.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 7
 
8.2%
부 6
 
7.1%
지 6
 
7.1%
양 6
 
7.1%
주 5
 
5.9%
영 4
 
4.7%
제 3
 
3.5%
천 3
 
3.5%
서 2
 
2.4%
울 2
 
2.4%
Other values (37) 41
48.2%

기관구분코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
국유림관리소
29 
지방청
6 
산림청
 
1

Length

Max length6
Median length6
Mean length5.4166667
Min length3

Unique

Unique1 ?
Unique (%)2.8%

Sample

1st row지방청
2nd row국유림관리소
3rd row국유림관리소
4th row국유림관리소
5th row국유림관리소

Common Values

ValueCountFrequency (%)
국유림관리소 29
80.6%
지방청 6
 
16.7%
산림청 1
 
2.8%

Length

2023-12-12T17:49:41.162115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:41.274694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국유림관리소 29
80.6%
지방청 6
 
16.7%
산림청 1
 
2.8%

상위기관ID
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
E00
7 
N00
7 
KFS
6 
S00
5 
W00
5 
Other values (3)
6 

Length

Max length4
Median length3
Mean length3.0277778
Min length3

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st rowKFS
2nd rowC00
3rd rowC00
4th rowC00
5th rowC00

Common Values

ValueCountFrequency (%)
E00 7
19.4%
N00 7
19.4%
KFS 6
16.7%
S00 5
13.9%
W00 5
13.9%
C00 4
11.1%
I00 1
 
2.8%
<NA> 1
 
2.8%

Length

2023-12-12T17:49:41.395610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:41.515377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e00 7
19.4%
n00 7
19.4%
kfs 6
16.7%
s00 5
13.9%
w00 5
13.9%
c00 4
11.1%
i00 1
 
2.8%
na 1
 
2.8%

정렬순서
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.583333
Minimum0
Maximum61
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:49:41.649669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.75
Q119.25
median30.5
Q343.25
95-th percentile56.25
Maximum61
Range61
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.134037
Coefficient of variation (CV)0.51084021
Kurtosis-0.95008554
Mean31.583333
Median Absolute Deviation (MAD)13
Skewness0.17352576
Sum1137
Variance260.30714
MonotonicityNot monotonic
2023-12-12T17:49:41.790719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
40 1
 
2.8%
13 1
 
2.8%
15 1
 
2.8%
16 1
 
2.8%
17 1
 
2.8%
30 1
 
2.8%
31 1
 
2.8%
32 1
 
2.8%
33 1
 
2.8%
34 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
0 1
2.8%
10 1
2.8%
11 1
2.8%
12 1
2.8%
13 1
2.8%
14 1
2.8%
15 1
2.8%
16 1
2.8%
17 1
2.8%
20 1
2.8%
ValueCountFrequency (%)
61 1
2.8%
60 1
2.8%
55 1
2.8%
54 1
2.8%
53 1
2.8%
52 1
2.8%
51 1
2.8%
50 1
2.8%
44 1
2.8%
43 1
2.8%

조직ID
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T17:49:41.982952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters252
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row1400461
2nd row1400464
3rd row1400465
4th row1400466
5th row1400467
ValueCountFrequency (%)
1400461 1
 
2.8%
1400464 1
 
2.8%
1400457 1
 
2.8%
1400442 1
 
2.8%
1400440 1
 
2.8%
1400439 1
 
2.8%
1400727 1
 
2.8%
1400453 1
 
2.8%
1400456 1
 
2.8%
1400458 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T17:49:42.279092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 82
32.5%
4 77
30.6%
1 42
16.7%
5 10
 
4.0%
6 9
 
3.6%
7 9
 
3.6%
8 6
 
2.4%
3 6
 
2.4%
2 6
 
2.4%
9 3
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 250
99.2%
Uppercase Letter 2
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 82
32.8%
4 77
30.8%
1 42
16.8%
5 10
 
4.0%
6 9
 
3.6%
7 9
 
3.6%
8 6
 
2.4%
3 6
 
2.4%
2 6
 
2.4%
9 3
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
M 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 250
99.2%
Latin 2
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 82
32.8%
4 77
30.8%
1 42
16.8%
5 10
 
4.0%
6 9
 
3.6%
7 9
 
3.6%
8 6
 
2.4%
3 6
 
2.4%
2 6
 
2.4%
9 3
 
1.2%
Latin
ValueCountFrequency (%)
M 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82
32.5%
4 77
30.6%
1 42
16.7%
5 10
 
4.0%
6 9
 
3.6%
7 9
 
3.6%
8 6
 
2.4%
3 6
 
2.4%
2 6
 
2.4%
9 3
 
1.2%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size168.0 B
True
36 
ValueCountFrequency (%)
True 36
100.0%
2023-12-12T17:49:42.380317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T17:49:39.496500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:49:42.435757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명기관약어명기관구분코드상위기관ID정렬순서조직ID
기관명1.0001.0001.0001.0001.0001.000
기관약어명1.0001.0001.0001.0001.0001.000
기관구분코드1.0001.0001.0001.0000.7681.000
상위기관ID1.0001.0001.0001.0000.8801.000
정렬순서1.0001.0000.7680.8801.0001.000
조직ID1.0001.0001.0001.0001.0001.000
2023-12-12T17:49:42.527092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상위기관ID기관구분코드
상위기관ID1.0000.921
기관구분코드0.9211.000
2023-12-12T17:49:42.614841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정렬순서기관구분코드상위기관ID
정렬순서1.0000.5690.700
기관구분코드0.5691.0000.921
상위기관ID0.7000.9211.000

Missing values

2023-12-12T17:49:39.673860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:49:39.797981image/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

기관명기관약어명기관구분코드상위기관ID정렬순서조직ID사용여부
0중부지방산림청중부지청지방청KFS401400461Y
1충주국유림관리소충주국유림관리소C00411400464Y
2보은국유림관리소보은국유림관리소C00421400465Y
3단양국유림관리소단양국유림관리소C00431400466Y
4부여국유림관리소부여국유림관리소C00441400467Y
5동부지방산림청동부지청지방청KFS201400444Y
6강릉국유림관리소강릉국유림관리소E00211400447Y
7양양국유림관리소양양국유림관리소E00221400481Y
8평창국유림관리소평창국유림관리소E00231400448Y
9영월국유림관리소영월국유림관리소E00241400449Y
기관명기관약어명기관구분코드상위기관ID정렬순서조직ID사용여부
26영덕국유림관리소영덕국유림관리소S00321400457Y
27구미국유림관리소구미국유림관리소S00331400458Y
28울진국유림관리소울진국유림관리소S00341400459Y
29양산국유림관리소양산국유림관리소S00351400460Y
30서부지방산림청서부지청지방청KFS501400468Y
31정읍국유림관리소정읍국유림관리소W00511400471Y
32무주국유림관리소무주국유림관리소W00521400472Y
33영암국유림관리소영암국유림관리소W00531400473Y
34순천국유림관리소순천국유림관리소W00541400482Y
35함양국유림관리소함양국유림관리소W00551400474Y