Overview

Dataset statistics

Number of variables4
Number of observations922
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.9 KiB
Average record size in memory32.1 B

Variable types

Text3
Categorical1

Dataset

Description스마트팜 코리아_아이덴티티 정보
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2646

Alerts

농가 ID has unique valuesUnique
시설 ID has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:49:58.821161
Analysis finished2024-03-13 11:49:59.174167
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

농가 ID
Text

UNIQUE 

Distinct922
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2024-03-13T20:49:59.420025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.9370933
Min length6

Characters and Unicode

Total characters9162
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique922 ?
Unique (%)100.0%

Sample

1st rowPFS_0000001
2nd rowPFS_0000002
3rd rowPFS_0000003
4th rowPFS_0000004
5th rowPFS_0000005
ValueCountFrequency (%)
pfs_0000001 1
 
0.1%
pf_0023374 1
 
0.1%
pf_0023611 1
 
0.1%
pf_0023377 1
 
0.1%
pf_0023378 1
 
0.1%
pf_0023379 1
 
0.1%
pf_0023380 1
 
0.1%
pf_0023381 1
 
0.1%
pf_0023382 1
 
0.1%
pf_0023383 1
 
0.1%
Other values (912) 912
98.9%
2024-03-13T20:49:59.907550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2790
30.5%
2 990
 
10.8%
P 905
 
9.9%
F 905
 
9.9%
_ 905
 
9.9%
3 635
 
6.9%
1 431
 
4.7%
6 305
 
3.3%
4 290
 
3.2%
7 276
 
3.0%
Other values (6) 730
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6403
69.9%
Uppercase Letter 1820
 
19.9%
Connector Punctuation 905
 
9.9%
Lowercase Letter 34
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2790
43.6%
2 990
 
15.5%
3 635
 
9.9%
1 431
 
6.7%
6 305
 
4.8%
4 290
 
4.5%
7 276
 
4.3%
8 238
 
3.7%
5 229
 
3.6%
9 219
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
P 905
49.7%
F 905
49.7%
S 10
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
s 17
50.0%
k 17
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7308
79.8%
Latin 1854
 
20.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2790
38.2%
2 990
 
13.5%
_ 905
 
12.4%
3 635
 
8.7%
1 431
 
5.9%
6 305
 
4.2%
4 290
 
4.0%
7 276
 
3.8%
8 238
 
3.3%
5 229
 
3.1%
Latin
ValueCountFrequency (%)
P 905
48.8%
F 905
48.8%
s 17
 
0.9%
k 17
 
0.9%
S 10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2790
30.5%
2 990
 
10.8%
P 905
 
9.9%
F 905
 
9.9%
_ 905
 
9.9%
3 635
 
6.9%
1 431
 
4.7%
6 305
 
3.3%
4 290
 
3.2%
7 276
 
3.0%
Other values (6) 730
 
8.0%

시설 ID
Text

UNIQUE 

Distinct922
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2024-03-13T20:50:00.180575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.937093
Min length9

Characters and Unicode

Total characters11928
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique922 ?
Unique (%)100.0%

Sample

1st rowPFS_0000001_01
2nd rowPFS_0000002_01
3rd rowPFS_0000003_01
4th rowPFS_0000004_01
5th rowPFS_0000005_01
ValueCountFrequency (%)
pfs_0000001_01 1
 
0.1%
pf_0023374_01 1
 
0.1%
pf_0023611_01 1
 
0.1%
pf_0023377_01 1
 
0.1%
pf_0023378_01 1
 
0.1%
pf_0023379_01 1
 
0.1%
pf_0023380_01 1
 
0.1%
pf_0023381_01 1
 
0.1%
pf_0023382_01 1
 
0.1%
pf_0023383_01 1
 
0.1%
Other values (912) 912
98.9%
2024-03-13T20:50:00.700014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3711
31.1%
_ 1827
15.3%
1 1354
 
11.4%
2 990
 
8.3%
P 905
 
7.6%
F 905
 
7.6%
3 634
 
5.3%
6 310
 
2.6%
4 282
 
2.4%
7 279
 
2.3%
Other values (6) 731
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8247
69.1%
Connector Punctuation 1827
 
15.3%
Uppercase Letter 1820
 
15.3%
Lowercase Letter 34
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3711
45.0%
1 1354
 
16.4%
2 990
 
12.0%
3 634
 
7.7%
6 310
 
3.8%
4 282
 
3.4%
7 279
 
3.4%
8 240
 
2.9%
5 230
 
2.8%
9 217
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
P 905
49.7%
F 905
49.7%
S 10
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
s 17
50.0%
k 17
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1827
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10074
84.5%
Latin 1854
 
15.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3711
36.8%
_ 1827
18.1%
1 1354
 
13.4%
2 990
 
9.8%
3 634
 
6.3%
6 310
 
3.1%
4 282
 
2.8%
7 279
 
2.8%
8 240
 
2.4%
5 230
 
2.3%
Latin
ValueCountFrequency (%)
P 905
48.8%
F 905
48.8%
s 17
 
0.9%
k 17
 
0.9%
S 10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3711
31.1%
_ 1827
15.3%
1 1354
 
11.4%
2 990
 
8.3%
P 905
 
7.6%
F 905
 
7.6%
3 634
 
5.3%
6 310
 
2.6%
4 282
 
2.4%
7 279
 
2.3%
Other values (6) 731
 
6.1%

법정동명
Text

Distinct123
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2024-03-13T20:50:01.061240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.3557484
Min length7

Characters and Unicode

Total characters7704
Distinct characters110
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

Unique35 ?
Unique (%)3.8%

Sample

1st row경상남도 사천시
2nd row전라북도 김제시
3rd row경상남도 함안군
4th row경상남도 진주시
5th row경상남도 진주시
ValueCountFrequency (%)
경상남도 205
 
11.1%
전라남도 181
 
9.8%
충청남도 151
 
8.1%
전라북도 142
 
7.7%
경상북도 80
 
4.3%
세종특별자치시 69
 
3.7%
논산시 56
 
3.0%
진주시 44
 
2.4%
김제시 43
 
2.3%
산청군 41
 
2.2%
Other values (129) 841
45.4%
2024-03-13T20:50:01.605906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
931
 
12.1%
도 839
 
10.9%
남 554
 
7.2%
군 472
 
6.1%
시 459
 
6.0%
전 324
 
4.2%
라 323
 
4.2%
경 322
 
4.2%
상 315
 
4.1%
북 238
 
3.1%
Other values (100) 2927
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6773
87.9%
Space Separator 931
 
12.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 839
 
12.4%
남 554
 
8.2%
군 472
 
7.0%
시 459
 
6.8%
전 324
 
4.8%
라 323
 
4.8%
경 322
 
4.8%
상 315
 
4.7%
북 238
 
3.5%
청 218
 
3.2%
Other values (99) 2709
40.0%
Space Separator
ValueCountFrequency (%)
931
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6773
87.9%
Common 931
 
12.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 839
 
12.4%
남 554
 
8.2%
군 472
 
7.0%
시 459
 
6.8%
전 324
 
4.8%
라 323
 
4.8%
경 322
 
4.8%
상 315
 
4.7%
북 238
 
3.5%
청 218
 
3.2%
Other values (99) 2709
40.0%
Common
ValueCountFrequency (%)
931
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6773
87.9%
ASCII 931
 
12.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
931
100.0%
Hangul
ValueCountFrequency (%)
도 839
 
12.4%
남 554
 
8.2%
군 472
 
7.0%
시 459
 
6.8%
전 324
 
4.8%
라 323
 
4.8%
경 322
 
4.8%
상 315
 
4.7%
북 238
 
3.5%
청 218
 
3.2%
Other values (99) 2709
40.0%

품목코드
Categorical

Distinct22
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
080400
430 
080300
253 
132600
91 
090100
63 
150400
 
19
Other values (17)
66 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique6 ?
Unique (%)0.7%

Sample

1st row080300
2nd row080300
3rd row080300
4th row080300
5th row132600

Common Values

ValueCountFrequency (%)
080400 430
46.6%
080300 253
27.4%
132600 91
 
9.9%
090100 63
 
6.8%
150400 19
 
2.1%
080200 17
 
1.8%
060300 10
 
1.1%
080600 7
 
0.8%
26E800 5
 
0.5%
065900 4
 
0.4%
Other values (12) 23
 
2.5%

Length

2024-03-13T20:50:01.805669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
080400 430
46.6%
080300 253
27.4%
132600 91
 
9.9%
090100 63
 
6.8%
150400 19
 
2.1%
080200 17
 
1.8%
060300 10
 
1.1%
080600 7
 
0.8%
26e800 5
 
0.5%
090300 4
 
0.4%
Other values (12) 23
 
2.5%

Missing values

2024-03-13T20:49:59.035402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:49:59.135119image/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

농가 ID시설 ID법정동명품목코드
0PFS_0000001PFS_0000001_01경상남도 사천시080300
1PFS_0000002PFS_0000002_01전라북도 김제시080300
2PFS_0000003PFS_0000003_01경상남도 함안군080300
3PFS_0000004PFS_0000004_01경상남도 진주시080300
4PFS_0000005PFS_0000005_01경상남도 진주시132600
5PFS_0000006PFS_0000006_01전라북도 순창군080300
6PFS_0000007PFS_0000007_01전라남도 화순군080300
7PFS_0000008PFS_0000008_01전라북도 완주군080300
8PFS_0000009PFS_0000009_01전라북도 김제시080300
9PFS_0000010PFS_0000010_01경상남도 거창군132600
농가 ID시설 ID법정동명품목코드
912sk0063sk0063_01세종특별자치시 연동면080300
913sk0065sk0065_01세종특별자치시 연동면080300
914sk0073sk0073_01세종특별자치시 연동면080300
915sk0078sk0078_01세종특별자치시 연동면080300
916sk0081sk0081_01세종특별자치시 연동면080300
917sk0085sk0085_01세종특별자치시 연동면080300
918sk0091sk0091_01세종특별자치시 연동면080300
919sk0102sk0102_01세종특별자치시 연동면080300
920sk0104sk0104_01세종특별자치시 연동면080300
921sk0118sk0118_01세종특별자치시 연동면080300