Overview

Dataset statistics

Number of variables4
Number of observations52
Missing cells12
Missing cells (%)5.8%
Duplicate rows1
Duplicate rows (%)1.9%
Total size in memory1.8 KiB
Average record size in memory34.5 B

Variable types

Categorical2
Text2

Alerts

Dataset has 1 (1.9%) duplicate rowsDuplicates
Unnamed: 2 has 6 (11.5%) missing valuesMissing
Unnamed: 3 has 6 (11.5%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:10:30.966435
Analysis finished2024-03-14 02:10:31.320698
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도유림 조림 현황
Categorical

Distinct19
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
6 
2001
4 
2004
4 
2012
4 
2006
4 
Other values (14)
30 

Length

Max length4
Median length4
Mean length3.9615385
Min length2

Unique

Unique5 ?
Unique (%)9.6%

Sample

1st row<NA>
2nd row<NA>
3rd row시행년도
4th row합계
5th row2001

Common Values

ValueCountFrequency (%)
<NA> 6
11.5%
2001 4
 
7.7%
2004 4
 
7.7%
2012 4
 
7.7%
2006 4
 
7.7%
2015 4
 
7.7%
2013 3
 
5.8%
2002 3
 
5.8%
2007 3
 
5.8%
2014 3
 
5.8%
Other values (9) 14
26.9%

Length

2024-03-14T11:10:31.598459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6
11.5%
2004 4
 
7.7%
2012 4
 
7.7%
2006 4
 
7.7%
2015 4
 
7.7%
2001 4
 
7.7%
2007 3
 
5.8%
2011 3
 
5.8%
2014 3
 
5.8%
2002 3
 
5.8%
Other values (9) 14
26.9%

Unnamed: 1
Categorical

Distinct20
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
진안군 백운면 신암리 산1
8 
<NA>
7 
진안군 백운면 신암리 산1-11
6 
완주군 동상면 대아리 산1-2
6 
장수군 장계면 명덕리 산154-1
4 
Other values (15)
21 

Length

Max length21
Median length18
Mean length15.019231
Min length4

Unique

Unique9 ?
Unique (%)17.3%

Sample

1st row<NA>
2nd row<NA>
3rd row위 치
4th row<NA>
5th row장수군 장계면 명덕리 산154-1

Common Values

ValueCountFrequency (%)
진안군 백운면 신암리 산1 8
15.4%
<NA> 7
13.5%
진안군 백운면 신암리 산1-11 6
11.5%
완주군 동상면 대아리 산1-2 6
11.5%
장수군 장계면 명덕리 산154-1 4
 
7.7%
완주군 운주면 고당리 산30 2
 
3.8%
진안군 백운면 신암리 산1, 산1-11 2
 
3.8%
진안군 백운면 노촌리 산1 2
 
3.8%
장수군 장계면 명덕리 산154-93 2
 
3.8%
진안군 백운면 신암리 산1외 1필 2
 
3.8%
Other values (10) 11
21.2%

Length

2024-03-14T11:10:31.700772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진안군 22
 
11.2%
백운면 22
 
11.2%
신암리 18
 
9.1%
산1 12
 
6.1%
완주군 11
 
5.6%
산1-11 8
 
4.1%
장수군 7
 
3.6%
명덕리 7
 
3.6%
장계면 7
 
3.6%
동상면 7
 
3.6%
Other values (35) 76
38.6%

Unnamed: 2
Text

MISSING 

Distinct26
Distinct (%)56.5%
Missing6
Missing (%)11.5%
Memory size548.0 B
2024-03-14T11:10:31.820547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1086957
Min length1

Characters and Unicode

Total characters189
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)39.1%

Sample

1st row사업량
2nd row369.3
3rd row2.4
4th row15.0
5th row10.0
ValueCountFrequency (%)
10.0 9
19.6%
5.0 4
 
8.7%
15.0 4
 
8.7%
3.0 3
 
6.5%
5 2
 
4.3%
20.0 2
 
4.3%
1.0 2
 
4.3%
2.0 2
 
4.3%
369.3 1
 
2.2%
3 1
 
2.2%
Other values (16) 16
34.8%
2024-03-14T11:10:32.061384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45
23.8%
. 40
21.2%
37
19.6%
1 20
10.6%
5 13
 
6.9%
2 13
 
6.9%
3 7
 
3.7%
8 3
 
1.6%
4 3
 
1.6%
6 3
 
1.6%
Other values (4) 5
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109
57.7%
Other Punctuation 40
 
21.2%
Space Separator 37
 
19.6%
Other Letter 3
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45
41.3%
1 20
18.3%
5 13
 
11.9%
2 13
 
11.9%
3 7
 
6.4%
8 3
 
2.8%
4 3
 
2.8%
6 3
 
2.8%
9 2
 
1.8%
Other Letter
ValueCountFrequency (%)
사 1
33.3%
업 1
33.3%
량 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%
Space Separator
ValueCountFrequency (%)
37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186
98.4%
Hangul 3
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45
24.2%
. 40
21.5%
37
19.9%
1 20
10.8%
5 13
 
7.0%
2 13
 
7.0%
3 7
 
3.8%
8 3
 
1.6%
4 3
 
1.6%
6 3
 
1.6%
Hangul
ValueCountFrequency (%)
사 1
33.3%
업 1
33.3%
량 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
98.4%
Hangul 3
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45
24.2%
. 40
21.5%
37
19.9%
1 20
10.8%
5 13
 
7.0%
2 13
 
7.0%
3 7
 
3.8%
8 3
 
1.6%
4 3
 
1.6%
6 3
 
1.6%
Hangul
ValueCountFrequency (%)
사 1
33.3%
업 1
33.3%
량 1
33.3%

Unnamed: 3
Text

MISSING 

Distinct43
Distinct (%)93.5%
Missing6
Missing (%)11.5%
Memory size548.0 B
2024-03-14T11:10:32.332654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.4782609
Min length4

Characters and Unicode

Total characters436
Distinct characters24
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)87.0%

Sample

1st row (단위 : ha)
2nd row사업기간
3rd row10.22~11.20
4th row2.26~4.16
5th row2.28~4.19
ValueCountFrequency (%)
03.30~05.16 2
 
4.2%
3.15~4.23 2
 
4.2%
3.26~4.24 2
 
4.2%
9.23~10.22 1
 
2.1%
3.25~4.23 1
 
2.1%
ha 1
 
2.1%
11.1~12.11 1
 
2.1%
03.08~04.27 1
 
2.1%
3.12~4.21 1
 
2.1%
3.17~4.8 1
 
2.1%
Other values (35) 35
72.9%
2024-03-14T11:10:32.692967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 88
20.2%
1 77
17.7%
2 47
10.8%
~ 44
10.1%
0 40
9.2%
3 38
8.7%
4 31
 
7.1%
5 17
 
3.9%
6 13
 
3.0%
8 10
 
2.3%
Other values (14) 31
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290
66.5%
Other Punctuation 89
 
20.4%
Math Symbol 44
 
10.1%
Other Letter 6
 
1.4%
Space Separator 3
 
0.7%
Lowercase Letter 2
 
0.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 77
26.6%
2 47
16.2%
0 40
13.8%
3 38
13.1%
4 31
10.7%
5 17
 
5.9%
6 13
 
4.5%
8 10
 
3.4%
9 10
 
3.4%
7 7
 
2.4%
Other Letter
ValueCountFrequency (%)
단 1
16.7%
위 1
16.7%
간 1
16.7%
기 1
16.7%
업 1
16.7%
사 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 88
98.9%
: 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
h 1
50.0%
Math Symbol
ValueCountFrequency (%)
~ 44
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 428
98.2%
Hangul 6
 
1.4%
Latin 2
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
. 88
20.6%
1 77
18.0%
2 47
11.0%
~ 44
10.3%
0 40
9.3%
3 38
8.9%
4 31
 
7.2%
5 17
 
4.0%
6 13
 
3.0%
8 10
 
2.3%
Other values (6) 23
 
5.4%
Hangul
ValueCountFrequency (%)
단 1
16.7%
위 1
16.7%
간 1
16.7%
기 1
16.7%
업 1
16.7%
사 1
16.7%
Latin
ValueCountFrequency (%)
a 1
50.0%
h 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430
98.6%
Hangul 6
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 88
20.5%
1 77
17.9%
2 47
10.9%
~ 44
10.2%
0 40
9.3%
3 38
8.8%
4 31
 
7.2%
5 17
 
4.0%
6 13
 
3.0%
8 10
 
2.3%
Other values (8) 25
 
5.8%
Hangul
ValueCountFrequency (%)
단 1
16.7%
위 1
16.7%
간 1
16.7%
기 1
16.7%
업 1
16.7%
사 1
16.7%

Correlations

2024-03-14T11:10:32.775332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도유림 조림 현황Unnamed: 1Unnamed: 2Unnamed: 3
도유림 조림 현황1.0000.8380.8120.990
Unnamed: 10.8381.0000.8560.897
Unnamed: 20.8120.8561.0000.774
Unnamed: 30.9900.8970.7741.000
2024-03-14T11:10:32.853131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1도유림 조림 현황
Unnamed: 11.0000.409
도유림 조림 현황0.4091.000
2024-03-14T11:10:32.926775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도유림 조림 현황Unnamed: 1
도유림 조림 현황1.0000.409
Unnamed: 10.4091.000

Missing values

2024-03-14T11:10:31.137223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:10:31.199071image/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.
2024-03-14T11:10:31.273583image/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

도유림 조림 현황Unnamed: 1Unnamed: 2Unnamed: 3
0<NA><NA><NA><NA>
1<NA><NA><NA>(단위 : ha)
2시행년도위 치사업량사업기간
3합계<NA>369.3<NA>
42001장수군 장계면 명덕리 산154-12.410.22~11.20
52001장수군 장계면 명덕리 산154-115.02.26~4.16
62001완주군 소양면 신촌리 산18-1외 2필10.02.28~4.19
72001진안군 백운면 신암리 산15.03.2~4.17
82002장수군 장계면 명덕리 산154-112.03.15~4.23
92002순창군 쌍치면 금성리 산434.610.23~11.22
도유림 조림 현황Unnamed: 1Unnamed: 2Unnamed: 3
422014진안군 백운면 신암리 산10.29.25~10.18
432015진안군 백운면 신암리 산1외 1필103.25~4.23
442015장수군 장계면 명덕리 산 154-133.26~4.24
452015진안군 백운면 노촌리 산153.26~4.24
462015진안군 백운면 신암리 산1-1159.23~10.22
472016진안군 백운면 신암리 산1외 1필203.25~5.8
48<NA><NA><NA><NA>
49<NA><NA><NA><NA>
50<NA><NA><NA><NA>
51<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

도유림 조림 현황Unnamed: 1Unnamed: 2Unnamed: 3# duplicates
0<NA><NA><NA><NA>5