Overview

Dataset statistics

Number of variables12
Number of observations1968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory186.6 KiB
Average record size in memory97.1 B

Variable types

Categorical3
Text9

Dataset

Description경상북도 시군단위지역내 총생산에 대한 데이터로 경제활동별 경상북도 시군단위 지역내총생산(GRDP)정보를 제공합니다.
Author경상북도
URLhttps://www.data.go.kr/data/15052025/fileData.do

Reproduction

Analysis started2023-12-16 15:15:20.925688
Analysis finished2023-12-16 15:15:24.873011
Duration3.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2018
408 
2020
408 
2015
384 
2016
384 
2017
384 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 408
20.7%
2020 408
20.7%
2015 384
19.5%
2016 384
19.5%
2017 384
19.5%

Length

2023-12-16T15:15:25.320357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:15:26.217111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 408
20.7%
2020 408
20.7%
2015 384
19.5%
2016 384
19.5%
2017 384
19.5%

시군구명
Categorical

Distinct24
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
포항시
 
82
경주시
 
82
김천시
 
82
안동시
 
82
구미시
 
82
Other values (19)
1558 

Length

Max length4
Median length3
Mean length3.0416667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포항시
2nd row경주시
3rd row김천시
4th row안동시
5th row구미시

Common Values

ValueCountFrequency (%)
포항시 82
 
4.2%
경주시 82
 
4.2%
김천시 82
 
4.2%
안동시 82
 
4.2%
구미시 82
 
4.2%
영주시 82
 
4.2%
영천시 82
 
4.2%
상주시 82
 
4.2%
문경시 82
 
4.2%
경산시 82
 
4.2%
Other values (14) 1148
58.3%

Length

2023-12-16T15:15:26.927422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포항시 82
 
4.2%
경주시 82
 
4.2%
울릉군 82
 
4.2%
울진군 82
 
4.2%
봉화군 82
 
4.2%
예천군 82
 
4.2%
칠곡군 82
 
4.2%
성주군 82
 
4.2%
고령군 82
 
4.2%
청도군 82
 
4.2%
Other values (14) 1148
58.3%

항목명
Categorical

Distinct24
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
사업서비스업
 
120
광업
 
120
제조업
 
120
전기가스증기및공기조절업
 
120
건설업
 
120
Other values (19)
1368 

Length

Max length19
Median length13
Mean length8.1097561
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row총부가가치(기초가격)
2nd row총부가가치(기초가격)
3rd row총부가가치(기초가격)
4th row총부가가치(기초가격)
5th row총부가가치(기초가격)

Common Values

ValueCountFrequency (%)
사업서비스업 120
 
6.1%
광업 120
 
6.1%
제조업 120
 
6.1%
전기가스증기및공기조절업 120
 
6.1%
건설업 120
 
6.1%
정보통신업 120
 
6.1%
금융 및 보험업 120
 
6.1%
부동산업 120
 
6.1%
보건업 및 사회복지서비스업 120
 
6.1%
교육 서비스업 96
 
4.9%
Other values (14) 792
40.2%

Length

2023-12-16T15:15:28.033911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 792
 
19.3%
행정 216
 
5.3%
사업서비스업 120
 
2.9%
임업 120
 
2.9%
사회복지서비스업 120
 
2.9%
부동산업 120
 
2.9%
보험업 120
 
2.9%
보건업 120
 
2.9%
금융 120
 
2.9%
정보통신업 120
 
2.9%
Other values (30) 2136
52.0%

산출액
Text

Distinct1954
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:29.884088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.7057927
Min length1

Characters and Unicode

Total characters11229
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1947 ?
Unique (%)98.9%

Sample

1st row51988087
2nd row27481061
3rd row12488047
4th row8576472
5th row79449359
ValueCountFrequency (%)
11
 
0.6%
24815 2
 
0.1%
12737 2
 
0.1%
449567 2
 
0.1%
24630 2
 
0.1%
36794 2
 
0.1%
65981 1
 
0.1%
6282472 1
 
0.1%
51988087 1
 
0.1%
68952 1
 
0.1%
Other values (1943) 1943
98.7%
2023-12-16T15:15:32.704864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1545
13.8%
2 1329
11.8%
4 1102
9.8%
3 1096
9.8%
6 1079
9.6%
5 1066
9.5%
7 1030
9.2%
8 1008
9.0%
9 987
8.8%
0 964
8.6%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11206
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1545
13.8%
2 1329
11.9%
4 1102
9.8%
3 1096
9.8%
6 1079
9.6%
5 1066
9.5%
7 1030
9.2%
8 1008
9.0%
9 987
8.8%
0 964
8.6%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1545
13.8%
2 1329
11.8%
4 1102
9.8%
3 1096
9.8%
6 1079
9.6%
5 1066
9.5%
7 1030
9.2%
8 1008
9.0%
9 987
8.8%
0 964
8.6%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1545
13.8%
2 1329
11.8%
4 1102
9.8%
3 1096
9.8%
6 1079
9.6%
5 1066
9.5%
7 1030
9.2%
8 1008
9.0%
9 987
8.8%
0 964
8.6%
Other values (2) 23
 
0.2%

중간투입액
Text

Distinct1947
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:34.085789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.386687
Min length1

Characters and Unicode

Total characters10601
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1933 ?
Unique (%)98.2%

Sample

1st row34623367
2nd row17951110
3rd row7251238
4th row3878528
5th row47442592
ValueCountFrequency (%)
11
 
0.6%
15462 2
 
0.1%
4457 2
 
0.1%
10237 2
 
0.1%
5462 2
 
0.1%
12721 2
 
0.1%
54197 2
 
0.1%
15843 2
 
0.1%
220664 2
 
0.1%
12158 2
 
0.1%
Other values (1936) 1939
98.5%
2023-12-16T15:15:36.560390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1515
14.3%
2 1190
11.2%
3 1089
10.3%
4 1049
9.9%
5 1010
9.5%
6 982
9.3%
8 973
9.2%
0 939
8.9%
7 922
8.7%
9 909
8.6%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10578
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1515
14.3%
2 1190
11.2%
3 1089
10.3%
4 1049
9.9%
5 1010
9.5%
6 982
9.3%
8 973
9.2%
0 939
8.9%
7 922
8.7%
9 909
8.6%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1515
14.3%
2 1190
11.2%
3 1089
10.3%
4 1049
9.9%
5 1010
9.5%
6 982
9.3%
8 973
9.2%
0 939
8.9%
7 922
8.7%
9 909
8.6%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1515
14.3%
2 1190
11.2%
3 1089
10.3%
4 1049
9.9%
5 1010
9.5%
6 982
9.3%
8 973
9.2%
0 939
8.9%
7 922
8.7%
9 909
8.6%
Other values (2) 23
 
0.2%

부가가치
Text

Distinct1940
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:38.448910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.4222561
Min length1

Characters and Unicode

Total characters10671
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1919 ?
Unique (%)97.5%

Sample

1st row17364720
2nd row9529951
3rd row5236809
4th row4697944
5th row32006767
ValueCountFrequency (%)
11
 
0.6%
12999 2
 
0.1%
11727 2
 
0.1%
14103 2
 
0.1%
12145 2
 
0.1%
62340 2
 
0.1%
27295 2
 
0.1%
14890 2
 
0.1%
4469 2
 
0.1%
34109 2
 
0.1%
Other values (1929) 1939
98.5%
2023-12-16T15:15:40.984277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1430
13.4%
2 1286
12.1%
3 1184
11.1%
4 1063
10.0%
5 1006
9.4%
7 1005
9.4%
6 966
9.1%
9 925
8.7%
0 896
8.4%
8 887
8.3%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10648
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1430
13.4%
2 1286
12.1%
3 1184
11.1%
4 1063
10.0%
5 1006
9.4%
7 1005
9.4%
6 966
9.1%
9 925
8.7%
0 896
8.4%
8 887
8.3%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10671
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1430
13.4%
2 1286
12.1%
3 1184
11.1%
4 1063
10.0%
5 1006
9.4%
7 1005
9.4%
6 966
9.1%
9 925
8.7%
0 896
8.4%
8 887
8.3%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1430
13.4%
2 1286
12.1%
3 1184
11.1%
4 1063
10.0%
5 1006
9.4%
7 1005
9.4%
6 966
9.1%
9 925
8.7%
0 896
8.4%
8 887
8.3%
Other values (2) 23
 
0.2%

고정자본소모
Text

Distinct1902
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:42.453436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.6458333
Min length1

Characters and Unicode

Total characters9143
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)94.0%

Sample

1st row3922436
2nd row2368409
3rd row1167242
4th row1352494
5th row9527168
ValueCountFrequency (%)
11
 
0.6%
1083 3
 
0.2%
1167 3
 
0.2%
5720 3
 
0.2%
853 3
 
0.2%
1130 3
 
0.2%
3773 3
 
0.2%
2158 2
 
0.1%
636 2
 
0.1%
1118 2
 
0.1%
Other values (1891) 1933
98.2%
2023-12-16T15:15:45.303900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1309
14.3%
2 1084
11.9%
3 949
10.4%
4 945
10.3%
5 880
9.6%
8 843
9.2%
6 842
9.2%
7 778
8.5%
0 764
8.4%
9 726
7.9%
Other values (2) 23
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9120
99.7%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1309
14.4%
2 1084
11.9%
3 949
10.4%
4 945
10.4%
5 880
9.6%
8 843
9.2%
6 842
9.2%
7 778
8.5%
0 764
8.4%
9 726
8.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9143
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1309
14.3%
2 1084
11.9%
3 949
10.4%
4 945
10.3%
5 880
9.6%
8 843
9.2%
6 842
9.2%
7 778
8.5%
0 764
8.4%
9 726
7.9%
Other values (2) 23
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1309
14.3%
2 1084
11.9%
3 949
10.4%
4 945
10.3%
5 880
9.6%
8 843
9.2%
6 842
9.2%
7 778
8.5%
0 764
8.4%
9 726
7.9%
Other values (2) 23
 
0.3%

지역내순생산
Text

Distinct1946
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:46.854145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.296748
Min length1

Characters and Unicode

Total characters10424
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1931 ?
Unique (%)98.1%

Sample

1st row13442283
2nd row7161541
3rd row4069567
4th row3345449
5th row22479600
ValueCountFrequency (%)
11
 
0.6%
10381 2
 
0.1%
38631 2
 
0.1%
40632 2
 
0.1%
9751 2
 
0.1%
8318 2
 
0.1%
42134 2
 
0.1%
5851 2
 
0.1%
10662 2
 
0.1%
43360 2
 
0.1%
Other values (1935) 1939
98.5%
2023-12-16T15:15:49.542622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1444
13.9%
2 1238
11.9%
3 1047
10.0%
6 1009
9.7%
4 1004
9.6%
5 972
9.3%
8 945
9.1%
7 944
9.1%
0 909
8.7%
9 889
8.5%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10401
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1444
13.9%
2 1238
11.9%
3 1047
10.1%
6 1009
9.7%
4 1004
9.7%
5 972
9.3%
8 945
9.1%
7 944
9.1%
0 909
8.7%
9 889
8.5%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1444
13.9%
2 1238
11.9%
3 1047
10.0%
6 1009
9.7%
4 1004
9.6%
5 972
9.3%
8 945
9.1%
7 944
9.1%
0 909
8.7%
9 889
8.5%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1444
13.9%
2 1238
11.9%
3 1047
10.0%
6 1009
9.7%
4 1004
9.6%
5 972
9.3%
8 945
9.1%
7 944
9.1%
0 909
8.7%
9 889
8.5%
Other values (2) 23
 
0.2%

기타생산세(기타생산보조금 공제)
Text

Distinct1151
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:51.622214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.058435
Min length1

Characters and Unicode

Total characters6019
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique875 ?
Unique (%)44.5%

Sample

1st row136922
2nd row87784
3rd row31792
4th row26172
5th row158525
ValueCountFrequency (%)
2 39
 
2.0%
3 25
 
1.3%
5 23
 
1.2%
4 23
 
1.2%
19
 
1.0%
15 14
 
0.7%
10 12
 
0.6%
7 12
 
0.6%
1 12
 
0.6%
27 12
 
0.6%
Other values (1116) 1777
90.3%
2023-12-16T15:15:54.097559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1009
16.8%
2 780
13.0%
3 650
10.8%
5 550
9.1%
4 524
8.7%
6 519
8.6%
7 496
8.2%
8 484
8.0%
0 448
7.4%
9 438
7.3%
Other values (2) 121
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5898
98.0%
Dash Punctuation 93
 
1.5%
Space Separator 28
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1009
17.1%
2 780
13.2%
3 650
11.0%
5 550
9.3%
4 524
8.9%
6 519
8.8%
7 496
8.4%
8 484
8.2%
0 448
7.6%
9 438
7.4%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1009
16.8%
2 780
13.0%
3 650
10.8%
5 550
9.1%
4 524
8.7%
6 519
8.6%
7 496
8.2%
8 484
8.0%
0 448
7.4%
9 438
7.3%
Other values (2) 121
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1009
16.8%
2 780
13.0%
3 650
10.8%
5 550
9.1%
4 524
8.7%
6 519
8.6%
7 496
8.2%
8 484
8.0%
0 448
7.4%
9 438
7.3%
Other values (2) 121
 
2.0%

지역내요소소득
Text

Distinct1945
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:15:56.715328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.2865854
Min length1

Characters and Unicode

Total characters10404
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1930 ?
Unique (%)98.1%

Sample

1st row13305361
2nd row7073758
3rd row4037775
4th row3319277
5th row22321075
ValueCountFrequency (%)
11
 
0.6%
7273 3
 
0.2%
7827 2
 
0.1%
10656 2
 
0.1%
90016 2
 
0.1%
6463 2
 
0.1%
22706 2
 
0.1%
109544 2
 
0.1%
10060 2
 
0.1%
58360 2
 
0.1%
Other values (1934) 1938
98.5%
2023-12-16T15:15:59.179311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1459
14.0%
2 1194
11.5%
3 1039
10.0%
6 1005
9.7%
4 996
9.6%
5 994
9.6%
0 937
9.0%
7 928
8.9%
9 926
8.9%
8 903
8.7%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10381
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1459
14.1%
2 1194
11.5%
3 1039
10.0%
6 1005
9.7%
4 996
9.6%
5 994
9.6%
0 937
9.0%
7 928
8.9%
9 926
8.9%
8 903
8.7%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1459
14.0%
2 1194
11.5%
3 1039
10.0%
6 1005
9.7%
4 996
9.6%
5 994
9.6%
0 937
9.0%
7 928
8.9%
9 926
8.9%
8 903
8.7%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1459
14.0%
2 1194
11.5%
3 1039
10.0%
6 1005
9.7%
4 996
9.6%
5 994
9.6%
0 937
9.0%
7 928
8.9%
9 926
8.9%
8 903
8.7%
Other values (2) 23
 
0.2%

산출액(2015년 기준년 연쇄가격)
Text

Distinct1956
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:16:00.919195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.6935976
Min length1

Characters and Unicode

Total characters11205
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1951 ?
Unique (%)99.1%

Sample

1st row48406839
2nd row26778213
3rd row11958259
4th row8229342
5th row80333626
ValueCountFrequency (%)
11
 
0.6%
453368 2
 
0.1%
17940 2
 
0.1%
23919 2
 
0.1%
262713 1
 
0.1%
34932 1
 
0.1%
86992 1
 
0.1%
104654 1
 
0.1%
61883 1
 
0.1%
83983 1
 
0.1%
Other values (1945) 1945
98.8%
2023-12-16T15:16:03.197545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1485
13.3%
2 1292
11.5%
3 1142
10.2%
4 1139
10.2%
6 1106
9.9%
5 1101
9.8%
7 1031
9.2%
0 987
8.8%
8 978
8.7%
9 921
8.2%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11182
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1485
13.3%
2 1292
11.6%
3 1142
10.2%
4 1139
10.2%
6 1106
9.9%
5 1101
9.8%
7 1031
9.2%
0 987
8.8%
8 978
8.7%
9 921
8.2%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1485
13.3%
2 1292
11.5%
3 1142
10.2%
4 1139
10.2%
6 1106
9.9%
5 1101
9.8%
7 1031
9.2%
0 987
8.8%
8 978
8.7%
9 921
8.2%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1485
13.3%
2 1292
11.5%
3 1142
10.2%
4 1139
10.2%
6 1106
9.9%
5 1101
9.8%
7 1031
9.2%
0 987
8.8%
8 978
8.7%
9 921
8.2%
Other values (2) 23
 
0.2%

부가가치(2015년 기준년 연쇄가격)
Text

Distinct1942
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2023-12-16T15:16:05.375101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.4151423
Min length1

Characters and Unicode

Total characters10657
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1923 ?
Unique (%)97.7%

Sample

1st row16500553
2nd row9456375
3rd row4931668
4th row4439053
5th row29524795
ValueCountFrequency (%)
11
 
0.6%
60547 2
 
0.1%
26870 2
 
0.1%
16457 2
 
0.1%
290 2
 
0.1%
14800 2
 
0.1%
98460 2
 
0.1%
5916 2
 
0.1%
10125 2
 
0.1%
14590 2
 
0.1%
Other values (1931) 1939
98.5%
2023-12-16T15:16:07.774457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1505
14.1%
2 1291
12.1%
3 1202
11.3%
4 1062
10.0%
5 998
9.4%
7 956
9.0%
9 942
8.8%
0 929
8.7%
6 903
8.5%
8 846
7.9%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10634
99.8%
Space Separator 12
 
0.1%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1505
14.2%
2 1291
12.1%
3 1202
11.3%
4 1062
10.0%
5 998
9.4%
7 956
9.0%
9 942
8.9%
0 929
8.7%
6 903
8.5%
8 846
8.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10657
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1505
14.1%
2 1291
12.1%
3 1202
11.3%
4 1062
10.0%
5 998
9.4%
7 956
9.0%
9 942
8.8%
0 929
8.7%
6 903
8.5%
8 846
7.9%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1505
14.1%
2 1291
12.1%
3 1202
11.3%
4 1062
10.0%
5 998
9.4%
7 956
9.0%
9 942
8.8%
0 929
8.7%
6 903
8.5%
8 846
7.9%
Other values (2) 23
 
0.2%

Correlations

2023-12-16T15:16:08.541079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시군구명항목명
연도1.0000.0000.573
시군구명0.0001.0000.000
항목명0.5730.0001.000
2023-12-16T15:16:08.992146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명항목명연도
시군구명1.0000.0000.000
항목명0.0001.0000.318
연도0.0000.3181.000
2023-12-16T15:16:09.492108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시군구명항목명
연도1.0000.0000.318
시군구명0.0001.0000.000
항목명0.3180.0001.000

Missing values

2023-12-16T15:15:23.092211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:15:24.193136image/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

연도시군구명항목명산출액중간투입액부가가치고정자본소모지역내순생산기타생산세(기타생산보조금 공제)지역내요소소득산출액(2015년 기준년 연쇄가격)부가가치(2015년 기준년 연쇄가격)
02018포항시총부가가치(기초가격)519880873462336717364720392243613442283136922133053614840683916500553
12018경주시총부가가치(기초가격)2748106117951110952995123684097161541877847073758267782139456375
22018김천시총부가가치(기초가격)124880477251238523680911672424069567317924037775119582594931668
32018안동시총부가가치(기초가격)8576472387852846979441352494334544926172331927782293424439053
42018구미시총부가가치(기초가격)794493594744259232006767952716822479600158525223210758033362629524795
52018영주시총부가가치(기초가격)731064144759152834726623517221120918539219266969950262730213
62018영천시총부가가치(기초가격)972755763335453394013781360261265322095259055893223483218850
72018상주시총부가가치(기초가격)442981023750142054795487848156694714183155276442478021935673
82018문경시총부가가치(기초가격)33275201726606160091538575812151579587120557032590211548504
92018경산시총부가가치(기초가격)1957389012236791733709915801615756938754725681466191310847141220
연도시군구명항목명산출액중간투입액부가가치고정자본소모지역내순생산기타생산세(기타생산보조금 공제)지역내요소소득산출액(2015년 기준년 연쇄가격)부가가치(2015년 기준년 연쇄가격)
19582020울릉군운수및창고업3382225762806032464815474768350039692
19592020울릉군숙박 및 음식점업359632349212471298794846994153302111638
19602020울릉군정보통신업849142734218157426445263986414504
19612020울릉군금융 및 보험업849621366361459590296580673505761
19622020울릉군부동산업3894152023755771798158164036482238
19632020울릉군사업서비스업14349662177281696603396024132786907
19642020울릉군공공행정,국방및사회보장 행정140237262371140015830855693255691128017102377
19652020울릉군교육서비스업(정부)135303501100291256877428772123449102
19662020울릉군보건업 및 사회복지서비스업755740073550206614841148471903377
19672020울릉군예술, 스포츠 및 여가관련 서비스331951834714847486599828698962768210913