Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells70
Missing cells (%)37.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory81.3 B

Variable types

Text6
Unsupported3

Dataset

Description시군별지적재조사사업대상현황201510
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202583

Alerts

구 분 has 2 (9.5%) missing valuesMissing
대상지 현황 has 1 (4.8%) missing valuesMissing
Unnamed: 2 has 1 (4.8%) missing valuesMissing
Unnamed: 3 has 1 (4.8%) missing valuesMissing
비 율(%) has 1 (4.8%) missing valuesMissing
Unnamed: 5 has 1 (4.8%) missing valuesMissing
Unnamed: 6 has 21 (100.0%) missing valuesMissing
Unnamed: 7 has 21 (100.0%) missing valuesMissing
Unnamed: 8 has 21 (100.0%) missing valuesMissing
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:13:48.111973
Analysis finished2024-03-14 02:13:48.766573
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing2
Missing (%)9.5%
Memory size300.0 B
2024-03-14T11:13:48.876771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8947368
Min length2

Characters and Unicode

Total characters55
Distinct characters33
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

Unique19 ?
Unique (%)100.0%

Sample

1st row합 계
2nd row전주시
3rd row완산구
4th row덕진구
5th row군산시
ValueCountFrequency (%)
합 1
 
5.0%
계 1
 
5.0%
고창군 1
 
5.0%
순창군 1
 
5.0%
임실군 1
 
5.0%
장수군 1
 
5.0%
무주군 1
 
5.0%
진안군 1
 
5.0%
완주군 1
 
5.0%
김제시 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T11:13:49.184111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 9
 
16.4%
시 6
 
10.9%
주 3
 
5.5%
산 3
 
5.5%
진 2
 
3.6%
창 2
 
3.6%
완 2
 
3.6%
구 2
 
3.6%
안 2
 
3.6%
수 1
 
1.8%
Other values (23) 23
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
98.2%
Space Separator 1
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 9
16.7%
시 6
 
11.1%
주 3
 
5.6%
산 3
 
5.6%
진 2
 
3.7%
창 2
 
3.7%
완 2
 
3.7%
구 2
 
3.7%
안 2
 
3.7%
수 1
 
1.9%
Other values (22) 22
40.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
98.2%
Common 1
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 9
16.7%
시 6
 
11.1%
주 3
 
5.6%
산 3
 
5.6%
진 2
 
3.7%
창 2
 
3.7%
완 2
 
3.7%
구 2
 
3.7%
안 2
 
3.7%
수 1
 
1.9%
Other values (22) 22
40.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
98.2%
ASCII 1
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 9
16.7%
시 6
 
11.1%
주 3
 
5.6%
산 3
 
5.6%
진 2
 
3.7%
창 2
 
3.7%
완 2
 
3.7%
구 2
 
3.7%
안 2
 
3.7%
수 1
 
1.9%
Other values (22) 22
40.7%
ASCII
ValueCountFrequency (%)
1
100.0%

대상지 현황
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2024-03-14T11:13:49.349150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1
Min length2

Characters and Unicode

Total characters62
Distinct characters14
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

Unique20 ?
Unique (%)100.0%

Sample

1st row지구수
2nd row7,255
3rd row924
4th row406
5th row518
ValueCountFrequency (%)
지구수 1
 
5.0%
7,255 1
 
5.0%
646 1
 
5.0%
192 1
 
5.0%
21 1
 
5.0%
529 1
 
5.0%
960 1
 
5.0%
871 1
 
5.0%
310 1
 
5.0%
426 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T11:13:49.610945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
16.1%
5 8
12.9%
1 8
12.9%
0 7
11.3%
6 6
9.7%
4 5
8.1%
7 4
 
6.5%
9 4
 
6.5%
8 3
 
4.8%
, 2
 
3.2%
Other values (4) 5
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57
91.9%
Other Letter 3
 
4.8%
Other Punctuation 2
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
17.5%
5 8
14.0%
1 8
14.0%
0 7
12.3%
6 6
10.5%
4 5
8.8%
7 4
 
7.0%
9 4
 
7.0%
8 3
 
5.3%
3 2
 
3.5%
Other Letter
ValueCountFrequency (%)
지 1
33.3%
구 1
33.3%
수 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59
95.2%
Hangul 3
 
4.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
16.9%
5 8
13.6%
1 8
13.6%
0 7
11.9%
6 6
10.2%
4 5
8.5%
7 4
 
6.8%
9 4
 
6.8%
8 3
 
5.1%
, 2
 
3.4%
Hangul
ValueCountFrequency (%)
지 1
33.3%
구 1
33.3%
수 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59
95.2%
Hangul 3
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
16.9%
5 8
13.6%
1 8
13.6%
0 7
11.9%
6 6
10.2%
4 5
8.5%
7 4
 
6.8%
9 4
 
6.8%
8 3
 
5.1%
, 2
 
3.4%
Hangul
ValueCountFrequency (%)
지 1
33.3%
구 1
33.3%
수 1
33.3%

Unnamed: 2
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2024-03-14T11:13:49.762094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1
Min length5

Characters and Unicode

Total characters122
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row필지수(필지)
2nd row558,818
3rd row66,077
4th row26,342
5th row39,735
ValueCountFrequency (%)
필지수(필지 1
 
5.0%
558,818 1
 
5.0%
41,876 1
 
5.0%
18,371 1
 
5.0%
1,327 1
 
5.0%
15,146 1
 
5.0%
41,768 1
 
5.0%
32,519 1
 
5.0%
29,001 1
 
5.0%
57,060 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T11:13:50.056266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 19
15.6%
1 15
12.3%
5 13
10.7%
7 11
9.0%
6 10
8.2%
3 10
8.2%
4 10
8.2%
9 8
6.6%
8 7
 
5.7%
0 6
 
4.9%
Other values (6) 13
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
78.7%
Other Punctuation 19
 
15.6%
Other Letter 5
 
4.1%
Open Punctuation 1
 
0.8%
Close Punctuation 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
15.6%
5 13
13.5%
7 11
11.5%
6 10
10.4%
3 10
10.4%
4 10
10.4%
9 8
8.3%
8 7
7.3%
0 6
 
6.2%
2 6
 
6.2%
Other Letter
ValueCountFrequency (%)
필 2
40.0%
지 2
40.0%
수 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117
95.9%
Hangul 5
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
, 19
16.2%
1 15
12.8%
5 13
11.1%
7 11
9.4%
6 10
8.5%
3 10
8.5%
4 10
8.5%
9 8
6.8%
8 7
 
6.0%
0 6
 
5.1%
Other values (3) 8
6.8%
Hangul
ValueCountFrequency (%)
필 2
40.0%
지 2
40.0%
수 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117
95.9%
Hangul 5
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 19
16.2%
1 15
12.8%
5 13
11.1%
7 11
9.4%
6 10
8.5%
3 10
8.5%
4 10
8.5%
9 8
6.8%
8 7
 
6.0%
0 6
 
5.1%
Other values (3) 8
6.8%
Hangul
ValueCountFrequency (%)
필 2
40.0%
지 2
40.0%
수 1
20.0%

Unnamed: 3
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2024-03-14T11:13:50.212907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.95
Min length3

Characters and Unicode

Total characters119
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row대장면적(천㎢)
2nd row426,819
3rd row20,426
4th row5,207
5th row15,219
ValueCountFrequency (%)
대장면적(천㎢ 1
 
5.0%
426,819 1
 
5.0%
50,668 1
 
5.0%
12,651 1
 
5.0%
491 1
 
5.0%
9,108 1
 
5.0%
24,903 1
 
5.0%
21,873 1
 
5.0%
72,730 1
 
5.0%
19,256 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T11:13:50.484029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 18
15.1%
1 15
12.6%
2 14
11.8%
6 13
10.9%
7 8
6.7%
5 8
6.7%
0 8
6.7%
9 8
6.7%
8 7
 
5.9%
4 7
 
5.9%
Other values (9) 13
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
78.2%
Other Punctuation 18
 
15.1%
Other Letter 5
 
4.2%
Close Punctuation 1
 
0.8%
Other Symbol 1
 
0.8%
Open Punctuation 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
16.1%
2 14
15.1%
6 13
14.0%
7 8
8.6%
5 8
8.6%
0 8
8.6%
9 8
8.6%
8 7
7.5%
4 7
7.5%
3 5
 
5.4%
Other Letter
ValueCountFrequency (%)
장 1
20.0%
천 1
20.0%
적 1
20.0%
면 1
20.0%
대 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Symbol
ValueCountFrequency (%)
㎢ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114
95.8%
Hangul 5
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
, 18
15.8%
1 15
13.2%
2 14
12.3%
6 13
11.4%
7 8
7.0%
5 8
7.0%
0 8
7.0%
9 8
7.0%
8 7
 
6.1%
4 7
 
6.1%
Other values (4) 8
7.0%
Hangul
ValueCountFrequency (%)
장 1
20.0%
천 1
20.0%
적 1
20.0%
면 1
20.0%
대 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113
95.0%
Hangul 5
 
4.2%
CJK Compat 1
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 18
15.9%
1 15
13.3%
2 14
12.4%
6 13
11.5%
7 8
7.1%
5 8
7.1%
0 8
7.1%
9 8
7.1%
8 7
 
6.2%
4 7
 
6.2%
Other values (3) 7
 
6.2%
Hangul
ValueCountFrequency (%)
장 1
20.0%
천 1
20.0%
적 1
20.0%
면 1
20.0%
대 1
20.0%
CJK Compat
ValueCountFrequency (%)
㎢ 1
100.0%

비 율(%)
Text

MISSING 

Distinct18
Distinct (%)90.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2024-03-14T11:13:50.699029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.55
Min length2

Characters and Unicode

Total characters71
Distinct characters13
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

Unique16 ?
Unique (%)80.0%

Sample

1st row필지
2nd row15.1
3rd row33.9
4th row38.6
5th row38.6
ValueCountFrequency (%)
38.6 2
 
10.0%
9.1 2
 
10.0%
16.2 1
 
5.0%
7.8 1
 
5.0%
13.2 1
 
5.0%
0.6 1
 
5.0%
28.3 1
 
5.0%
13 1
 
5.0%
11.2 1
 
5.0%
15.1 1
 
5.0%
Other values (8) 8
40.0%
2024-03-14T11:13:50.996637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 18
25.4%
3 12
16.9%
1 12
16.9%
8 5
 
7.0%
6 5
 
7.0%
2 5
 
7.0%
9 4
 
5.6%
7 3
 
4.2%
4 2
 
2.8%
0 2
 
2.8%
Other values (3) 3
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
71.8%
Other Punctuation 18
 
25.4%
Other Letter 2
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12
23.5%
1 12
23.5%
8 5
9.8%
6 5
9.8%
2 5
9.8%
9 4
 
7.8%
7 3
 
5.9%
4 2
 
3.9%
0 2
 
3.9%
5 1
 
2.0%
Other Letter
ValueCountFrequency (%)
필 1
50.0%
지 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
97.2%
Hangul 2
 
2.8%

Most frequent character per script

Common
ValueCountFrequency (%)
. 18
26.1%
3 12
17.4%
1 12
17.4%
8 5
 
7.2%
6 5
 
7.2%
2 5
 
7.2%
9 4
 
5.8%
7 3
 
4.3%
4 2
 
2.9%
0 2
 
2.9%
Hangul
ValueCountFrequency (%)
필 1
50.0%
지 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
97.2%
Hangul 2
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 18
26.1%
3 12
17.4%
1 12
17.4%
8 5
 
7.2%
6 5
 
7.2%
2 5
 
7.2%
9 4
 
5.8%
7 3
 
4.3%
4 2
 
2.9%
0 2
 
2.9%
Hangul
ValueCountFrequency (%)
필 1
50.0%
지 1
50.0%

Unnamed: 5
Text

MISSING 

Distinct19
Distinct (%)95.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2024-03-14T11:13:51.138715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.15
Min length2

Characters and Unicode

Total characters63
Distinct characters12
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

Unique18 ?
Unique (%)90.0%

Sample

1st row면적
2nd row5.3
3rd row9.9
4th row5.4
5th row13.7
ValueCountFrequency (%)
2.1 2
 
10.0%
면적 1
 
5.0%
2.5 1
 
5.0%
0.1 1
 
5.0%
1.7 1
 
5.0%
3.9 1
 
5.0%
2.7 1
 
5.0%
8.9 1
 
5.0%
3.4 1
 
5.0%
5.2 1
 
5.0%
Other values (9) 9
45.0%
2024-03-14T11:13:51.430201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 19
30.2%
2 10
15.9%
3 8
12.7%
1 6
 
9.5%
5 5
 
7.9%
9 4
 
6.3%
7 3
 
4.8%
8 3
 
4.8%
4 2
 
3.2%
면 1
 
1.6%
Other values (2) 2
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42
66.7%
Other Punctuation 19
30.2%
Other Letter 2
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
23.8%
3 8
19.0%
1 6
14.3%
5 5
11.9%
9 4
 
9.5%
7 3
 
7.1%
8 3
 
7.1%
4 2
 
4.8%
0 1
 
2.4%
Other Letter
ValueCountFrequency (%)
면 1
50.0%
적 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61
96.8%
Hangul 2
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 19
31.1%
2 10
16.4%
3 8
13.1%
1 6
 
9.8%
5 5
 
8.2%
9 4
 
6.6%
7 3
 
4.9%
8 3
 
4.9%
4 2
 
3.3%
0 1
 
1.6%
Hangul
ValueCountFrequency (%)
면 1
50.0%
적 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61
96.8%
Hangul 2
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 19
31.1%
2 10
16.4%
3 8
13.1%
1 6
 
9.8%
5 5
 
8.2%
9 4
 
6.6%
7 3
 
4.9%
8 3
 
4.9%
4 2
 
3.3%
0 1
 
1.6%
Hangul
ValueCountFrequency (%)
면 1
50.0%
적 1
50.0%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

Correlations

2024-03-14T11:13:51.509595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분대상지 현황Unnamed: 2Unnamed: 3비 율(%)Unnamed: 5
구 분1.0001.0001.0001.0001.0001.000
대상지 현황1.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0001.0001.000
비 율(%)1.0001.0001.0001.0001.0000.939
Unnamed: 51.0001.0001.0001.0000.9391.000

Missing values

2024-03-14T11:13:48.401258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:13:48.593148image/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.
2024-03-14T11:13:48.690526image/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

구 분대상지 현황Unnamed: 2Unnamed: 3비 율(%)Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA>지구수필지수(필지)대장면적(천㎢)필지면적<NA><NA><NA>
1합 계7,255558,818426,81915.15.3<NA><NA><NA>
2전주시92466,07720,42633.99.9<NA><NA><NA>
3완산구40626,3425,20738.65.4<NA><NA><NA>
4덕진구51839,73515,21938.613.7<NA><NA><NA>
5군산시53735,49912,44914.43.2<NA><NA><NA>
6익산시1,020131,695117,92633.823.2<NA><NA><NA>
7본청60877,14466,27031.721.8<NA><NA><NA>
8함열41254,55151,65637.325.3<NA><NA><NA>
9정읍시5739,45437,83410.25.2<NA><NA><NA>
구 분대상지 현황Unnamed: 2Unnamed: 3비 율(%)Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
11김제시42657,06019,25616.23.4<NA><NA><NA>
12완주군31029,00172,73011.28.9<NA><NA><NA>
13진안군87132,51921,873132.7<NA><NA><NA>
14무주군96041,76824,90328.33.9<NA><NA><NA>
15장수군52915,1469,1089.11.7<NA><NA><NA>
16임실군211,3274910.60.1<NA><NA><NA>
17순창군19218,37112,6519.12.5<NA><NA><NA>
18고창군64641,87650,66813.28.3<NA><NA><NA>
19부안군25218,75910,6287.82.1<NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA>